Text summarization is the process to keep the relevant important information of the original text in a shorter version with the main ideas of the original text . There are two main classifications of text summarization process Extractive and Abstractive text summarization , Extractive smnmarization processes by using most important fragments of existing words , phrases or sentences from the original document , A sentence based mode ) using Fuzzy C-Means clustering has been proposed in this research . Six best key features including a new feature “ Sentence Highlighter Feature ” have been introduced for the sentence scoring . Mostly statistical features have been used for scoring the sentences . We are proposing a hybrid model for a single text document summarization . This hybrid model is an extraction based approach , which is combination of Statistical and semantic technique . section : INTRODUCTION Neural network models have recently made tremendous progress in a variety of NLP applications such as speech recognition [ reference ] , sentiment analysis [ reference ] ) , text summarization [ reference ] , and machine translation [ reference ] . Despite the overwhelming success achieved by recurrent neural networks in modeling long range dependencies between words , current recurrent neural network language models ( RNNLM ) are based on the conventional classification framework , which has two major drawbacks : First , there is no assumed metric on the output classes , whereas there is evidence suggesting that learning is improved when one can define a natural metric on the output space [ reference ] . We present our methodology for the generalization and fusion of conceptsWe define the notion of sentence generalizationThis work should be useful in intelligent systemsWe have proposed three approaches to find the one that will perform betterThe ML-based approach has benefited from the strength of two features Text summarization is either extractive or abstractive . Extractive summarization is to select the most salient pieces of information ( words , phrases , and/or sentences ) from a source document without adding any external information . Abstractive summarization allows an internal representation of the source document so as to produce a faithful summary of the source . In this paper , we formulate a sparse optimization framework for extractive document summarization . The proposed framework has a decomposable convex objective function . Extractive document summarization is a fundamental technique for document summarization . Most well-known approaches to extractive document summarization utilize supervised learning where algorithms are trained on collections of " ground truth " summaries built for a relatively large number of documents . We present an approach for extractive single-document summarization . Our approach is based on a weighted graphical representation of documents obtained by topic modeling . In this paper , we propose a multi-roles graph model for extractive single-document summarization . In our model , we consider that each text can be expressed in some important words which we call roles . We present two novel and contrasting Recurrent Neural Network ( RNN ) based architectures for extractive summarization of documents . The Classifier based architecture sequentially accepts or rejects each sentence in the original document order for its membership in the summary . In recent years , graph-based models and ranking algorithms have drawn considerable attention from the extractive document summarization community . Most existing approaches take into account sentence-level relations ( e.g. sentence similarity ) but neglect the difference among documents and the influence of documents on sentences . A system allowing extractive automatic summarization of textual documents is presented . The system is based on the cohesive properties of text , namely lexical chains , co-reference chains and named entity chains . Query focused multi-document summarization is a process of automatic query biased text compression of a document set . Lately , the graph-based and ranking methods have been intensively attracted the researchers from extractive document summarization domain . The uniform sentence connecteness or non-uniform document-sentence connecteness , such as sentence similarity weighted by document importance , were the main features used by work to date . In the current scenario of Information Technology , excessive and vast information is available on online resources but it is not always easy to find relevant and useful information . Along this issue , the paper is presented a method on extractive single document text summarization using Deep Learning method - Self-Organizing Maps ( SOM ) which is an unsupervised method and Artificial Neural Networks ( ANN ) which is a supervised method . The work involves investigating the effect of adding mapped sentences from SOM visualization , and re-training the inputs on ANN for ranking the sentences . In this paper , we introduce and compare between two novel approaches , supervised and unsupervised , for identifying the keywords to be used in extractive summarization of text documents . Both our approaches are based on the graph-based syntactic representation of text and web documents , which enhances the traditional vector-space model by taking into account some structural document features . This paper introduces a novel solution to target-oriented ( i.e. aspect-based ) sentiment summarization and SA of short informal texts with a main focus on Twitter posts known as " tweets " . We compare different algorithms and methods for SA polarity detection and sentiment summarization . We show that our hybrid polarity detection system not only outperforms the unigram state-of-the-art baseline , but also could be an advantage over other methods when used as a part of a sentiment summarization system . We compare different algorithms and methods for SA polarity detection and sentiment summarization . We show that our hybrid polarity detection system not only outperforms the unigram state-of-the-art baseline , but also could be an advantage over other methods when used as a part of a sentiment summarization system . Additionally , we illustrate that our SA and summarization system exhibits a high performance with various useful functionalities and features . We derive efficient algorithms to compute the forward and backward passes of our attention mechanisms , enabling their use in a neural network trained with backpropagation . To showcase their potential as a drop-in replacement for existing ones , we evaluate our attention mechanisms on three large-scale tasks : textual entailment , machine translation , and sentence summarization . Our attention mechanisms improve interpretability without sacrificing performance ; notably , on textual entailment and summarization , we outperform the standard attention mechanisms based on softmax and sparsemax . Sentence-based extractive summarization aims at automatically generating shorter versions of texts by extracting from them the minimal set of sentences that are necessary and sufficient to cover their content . Providing effective solutions to this task would allow the users to save time in selecting the most appropriate documents to read for satisfying their information needs or for supporting their decision-making tasks . We examine the effect of probabilistic topic model-based word representations , on sentence-based extractive summarization . We formulate the task of sentence selection as a binary classification problem , and we test a variety of machine learning algorithms , exploring a range of different settings for classification and modelling . Such collections sometimes have few categories and little hierarchy , or they have so much hierarchy that valuable relations between documents can easily become obscured . We describe here how our work in the area of term-recognition and sentence-based summarization can be used to filter the document lists that we return from searches . We can thus remove or downgrade the ranking of some documents that have limited utility even though they may match many of the search terms fairly accurately . A text is given to the computer and the computer returns a required extract of the original text document . Our methods on the sentence extraction-based text summarization task use the graph based algorithm to calculate importance of each sentence in document and most important sentences are extracted to generate document summary . These extraction based text summarization methods give an indexing weight to the document terms to compute the similarity values between sentences A text is given to the computer and the computer returns a required extract of the original text document . Our methods on the sentence extraction-based text summarization task use the graph based algorithm to calculate importance of each sentence in document and most important sentences are extracted to generate document summary . These extraction based text summarization methods give an indexing weight to the document terms to compute the similarity values between sentences . Teaching machine to understand needs to design an algorithm for the machine to comprehend documents . As some traditional methods can not learn the inherent characters effectively , this paper presents a new hybrid neural network model to extract sentence-level summarization from single document , and it allows us to develop an attention based deep neural network that can learn to understand documents with minimal prior knowledge . The proposed model composed of multiple processing layers can learn the representations of features . Evaluation results show that our summmarizer is able to surpass top performing systems participated at Text analysis conference 2008 . Gap between oracle summaries and state of art summaries is analyzed to depict the scope of improvement in sentence extractive summarization . To fully integrate the relatedness and advantages of both approaches , we propose in this paper a general framework for abstractive summarization which incorporates extractive summarization as an auxiliary task . In particular , our framework is composed of a shared hierarchical document encoder , an attention-based decoder for abstractive summarization , and an extractor for sentence-level extractive summarization . Learning these two tasks jointly with the shared encoder allows us to better capture the semantics in the document . In order to evaluate the method , we applied the results of topic detection to extractive multi-document summarization . The results showed that the method was effective for sentence selection in summarization . This implies that sentiment-based graph ’s pattern and score are helpful in generating a compact summary . The analysis will alleviate future research on sentiment-based summarization and motivates a new method which can be considered as a graph-based summarization to extract a summary based on its sentiment score . Afterwards , semantic compositional approaches are used to compute the vector of a sentence / document from the vectors of its constituents based on the principle of compositionality . Representative compositional approaches to learn sentence representation include recursive neural networks , convolutional neural network , long short - term memory and tree - structured LSTM . There also exists some studies focusing on learning continuous representation of documents . section : Summarization Finally , we evaluate our model on abstractive sentence summarization which takes a long sentence as input and outputs a shortened version . The current best models on this task are recurrent neural networks which either optimize the evaluation metric [ reference ] or address specific problems of summarization such as avoiding repeated generations section : ATTENTION Attentive neural networks have recently demonstrated success in a wide range of tasks ranging from handwriting synthesis [ reference ] , digit classification [ reference ] , machine translation , image captioning [ reference ] , speech recognition [ reference ] and sentence summarization [ reference ] , to geometric reasoning [ reference ] . But it suffers from the drawback of generation of repeated phrases . In this work we propose a model for the query-based summarization task based on the encode-attend-decode paradigm with two key additions ( i ) a query attention model ( in addition to document attention model ) which learns to focus on different portions of the query at different time steps ( instead of using a static representation for the query ) and ( ii ) a new diversity based attention model which aims to alleviate the problem of repeating phrases in the summary . In order to enable the testing of this model we introduce a new query-based summarization dataset building on debatepedia . Abstract The rise in the amount of textual resources available on the Internet has created the need for tools of automatic document summarization . The main challenges of query-oriented extractive summarization are ( 1 ) to identify the topics of the documents and ( 2 ) to recover query-relevant sentences of the documents that together cover these topics . Existing graph- or hypergraph-based summarizers use graph-based ranking algorithms to produce individual scores of relevance for the sentences . Especially , in the case of query-based extractive online forum summarization , curating complex information spread along multiple posts from multiple forum members to create a short meta-summary that answers a given query is a very challenging task . To overcome this challenge , we explore the applicability of microtask crowdsourcing as a fast and cheap alternative for query-based extractive text summarization of online forum discussions . We measure the linguistic quality of crowd-based forum summarizations , which is usually conducted in a traditional laboratory environment with the help of experts , via comparative crowdsourcing and laboratory experiments . To our knowledge , no other study considered query-based extractive text summarization and summary quality evaluation as an application area of the microtask crowdsourcing . By conducting experiments both in crowdsourcing and laboratory environments , and comparing the results of linguistic quality judgments , we found out that microtask crowdsourcing shows high applicability for determining the factors overall quality , grammaticality , non-redundancy , referential clarity , focus , and structure & coherence . In this paper we use a deep auto-encoder for extractive query-based summarization . We experiment with different input representations in order to overcome the problems stemming from sparse inputs characteristic to linguistic data . This paper explores the impact of several supervised machine learning approaches for extracting multi-document summaries for given queries . In particular , we compare classification and regression approaches for query-based extractive summarisation using data provided by the BioASQ Challenge . We tackled the problem of annotating sentences for training classification systems and show that a simple annotation approach outperforms regression-based summarisation . Query-focused summarization laid emphasis on user 's concern in the document . An approach to query-focused automatic summarization is presented . It utilizes search engine as tool for seeking answer . The most important step of query-focused extractive summarization is deciding which sentences are appropriately included in the final summary . In this paper , we propose a feature fusion based sentence selecting strategy , to identify the sentences with high query-relevance and high information density . Abstract In the paper , how to meet the users ’ need plays a very important role in the study on the query-oriented automatic summarization . Combining with the influence of text granularities and query information , we construct a model to explain the relationship between text granularities , point out the way based on the semantic relation to test text similarity , adopt a strategy to sentence dynamically allocation to produce summarization . Query Specific Document Summarization may provide a solution to handle large amount of information present on the World Wide Web . In this TEXT BOOK the work is on query specific extractive text summarization , wherein , sentences are scored based on certain scoring methods and those having high score are extracted to generate summary . SeeLM17 . HasselqvistHK17 and McCannKXS18 had introduced its RNN - based mechanism to query - based abstractive summarization and question answering , respectively ; however , their models can not handle multiple passages effectively . We extend the mechanism to a Transformer VaswaniSPUJGKP17 based one that allows words to be generated from a fixed vocabulary and words to be copied from both the question and multiple passages .
Text summarization is the process to keep the relevant important information of the original text in a shorter version with the main ideas of the original text . There are two main classifications of text summarization process Extractive and Abstractive text summarization , Extractive smnmarization processes by using most important fragments of existing words , phrases or sentences from the original document , A sentence based mode ) using Fuzzy C-Means clustering has been proposed in this research . Six best key features including a new feature “ Sentence Highlighter Feature ” have been introduced for the sentence scoring .
Mostly statistical features have been used for scoring the sentences . We are proposing a hybrid model for a single text document summarization . This hybrid model is an extraction based approach , which is combination of Statistical and semantic technique .
section : INTRODUCTION Neural network models have recently made tremendous progress in a variety of NLP applications such as speech recognition [ reference ] , sentiment analysis [ reference ] ) , text summarization [ reference ] , and machine translation [ reference ] . Despite the overwhelming success achieved by recurrent neural networks in modeling long range dependencies between words , current recurrent neural network language models ( RNNLM ) are based on the conventional classification framework , which has two major drawbacks : First , there is no assumed metric on the output classes , whereas there is evidence suggesting that learning is improved when one can define a natural metric on the output space [ reference ] .
We present our methodology for the generalization and fusion of conceptsWe define the notion of sentence generalizationThis work should be useful in intelligent systemsWe have proposed three approaches to find the one that will perform betterThe ML-based approach has benefited from the strength of two features Text summarization is either extractive or abstractive . Extractive summarization is to select the most salient pieces of information ( words , phrases , and/or sentences ) from a source document without adding any external information . Abstractive summarization allows an internal representation of the source document so as to produce a faithful summary of the source .
In this paper , we formulate a sparse optimization framework for extractive document summarization . The proposed framework has a decomposable convex objective function .
Extractive document summarization is a fundamental technique for document summarization . Most well-known approaches to extractive document summarization utilize supervised learning where algorithms are trained on collections of " ground truth " summaries built for a relatively large number of documents .
We present an approach for extractive single-document summarization . Our approach is based on a weighted graphical representation of documents obtained by topic modeling .
In this paper , we propose a multi-roles graph model for extractive single-document summarization . In our model , we consider that each text can be expressed in some important words which we call roles .
We present two novel and contrasting Recurrent Neural Network ( RNN ) based architectures for extractive summarization of documents . The Classifier based architecture sequentially accepts or rejects each sentence in the original document order for its membership in the summary .
In recent years , graph-based models and ranking algorithms have drawn considerable attention from the extractive document summarization community . Most existing approaches take into account sentence-level relations ( e.g. sentence similarity ) but neglect the difference among documents and the influence of documents on sentences .
A system allowing extractive automatic summarization of textual documents is presented . The system is based on the cohesive properties of text , namely lexical chains , co-reference chains and named entity chains .
Query focused multi-document summarization is a process of automatic query biased text compression of a document set . Lately , the graph-based and ranking methods have been intensively attracted the researchers from extractive document summarization domain . The uniform sentence connecteness or non-uniform document-sentence connecteness , such as sentence similarity weighted by document importance , were the main features used by work to date .
In the current scenario of Information Technology , excessive and vast information is available on online resources but it is not always easy to find relevant and useful information . Along this issue , the paper is presented a method on extractive single document text summarization using Deep Learning method - Self-Organizing Maps ( SOM ) which is an unsupervised method and Artificial Neural Networks ( ANN ) which is a supervised method . The work involves investigating the effect of adding mapped sentences from SOM visualization , and re-training the inputs on ANN for ranking the sentences .
In this paper , we introduce and compare between two novel approaches , supervised and unsupervised , for identifying the keywords to be used in extractive summarization of text documents . Both our approaches are based on the graph-based syntactic representation of text and web documents , which enhances the traditional vector-space model by taking into account some structural document features .
This paper introduces a novel solution to target-oriented ( i.e. aspect-based ) sentiment summarization and SA of short informal texts with a main focus on Twitter posts known as " tweets " . We compare different algorithms and methods for SA polarity detection and sentiment summarization . We show that our hybrid polarity detection system not only outperforms the unigram state-of-the-art baseline , but also could be an advantage over other methods when used as a part of a sentiment summarization system .
We compare different algorithms and methods for SA polarity detection and sentiment summarization . We show that our hybrid polarity detection system not only outperforms the unigram state-of-the-art baseline , but also could be an advantage over other methods when used as a part of a sentiment summarization system . Additionally , we illustrate that our SA and summarization system exhibits a high performance with various useful functionalities and features .
We derive efficient algorithms to compute the forward and backward passes of our attention mechanisms , enabling their use in a neural network trained with backpropagation . To showcase their potential as a drop-in replacement for existing ones , we evaluate our attention mechanisms on three large-scale tasks : textual entailment , machine translation , and sentence summarization . Our attention mechanisms improve interpretability without sacrificing performance ; notably , on textual entailment and summarization , we outperform the standard attention mechanisms based on softmax and sparsemax .
Sentence-based extractive summarization aims at automatically generating shorter versions of texts by extracting from them the minimal set of sentences that are necessary and sufficient to cover their content . Providing effective solutions to this task would allow the users to save time in selecting the most appropriate documents to read for satisfying their information needs or for supporting their decision-making tasks .
We examine the effect of probabilistic topic model-based word representations , on sentence-based extractive summarization . We formulate the task of sentence selection as a binary classification problem , and we test a variety of machine learning algorithms , exploring a range of different settings for classification and modelling .
Such collections sometimes have few categories and little hierarchy , or they have so much hierarchy that valuable relations between documents can easily become obscured . We describe here how our work in the area of term-recognition and sentence-based summarization can be used to filter the document lists that we return from searches . We can thus remove or downgrade the ranking of some documents that have limited utility even though they may match many of the search terms fairly accurately .
A text is given to the computer and the computer returns a required extract of the original text document . Our methods on the sentence extraction-based text summarization task use the graph based algorithm to calculate importance of each sentence in document and most important sentences are extracted to generate document summary . These extraction based text summarization methods give an indexing weight to the document terms to compute the similarity values between sentences
A text is given to the computer and the computer returns a required extract of the original text document . Our methods on the sentence extraction-based text summarization task use the graph based algorithm to calculate importance of each sentence in document and most important sentences are extracted to generate document summary . These extraction based text summarization methods give an indexing weight to the document terms to compute the similarity values between sentences .
Teaching machine to understand needs to design an algorithm for the machine to comprehend documents . As some traditional methods can not learn the inherent characters effectively , this paper presents a new hybrid neural network model to extract sentence-level summarization from single document , and it allows us to develop an attention based deep neural network that can learn to understand documents with minimal prior knowledge . The proposed model composed of multiple processing layers can learn the representations of features .
Evaluation results show that our summmarizer is able to surpass top performing systems participated at Text analysis conference 2008 . Gap between oracle summaries and state of art summaries is analyzed to depict the scope of improvement in sentence extractive summarization .
To fully integrate the relatedness and advantages of both approaches , we propose in this paper a general framework for abstractive summarization which incorporates extractive summarization as an auxiliary task . In particular , our framework is composed of a shared hierarchical document encoder , an attention-based decoder for abstractive summarization , and an extractor for sentence-level extractive summarization . Learning these two tasks jointly with the shared encoder allows us to better capture the semantics in the document .
In order to evaluate the method , we applied the results of topic detection to extractive multi-document summarization . The results showed that the method was effective for sentence selection in summarization .
This implies that sentiment-based graph ’s pattern and score are helpful in generating a compact summary . The analysis will alleviate future research on sentiment-based summarization and motivates a new method which can be considered as a graph-based summarization to extract a summary based on its sentiment score .
Afterwards , semantic compositional approaches are used to compute the vector of a sentence / document from the vectors of its constituents based on the principle of compositionality . Representative compositional approaches to learn sentence representation include recursive neural networks , convolutional neural network , long short - term memory and tree - structured LSTM . There also exists some studies focusing on learning continuous representation of documents .
section : Summarization Finally , we evaluate our model on abstractive sentence summarization which takes a long sentence as input and outputs a shortened version . The current best models on this task are recurrent neural networks which either optimize the evaluation metric [ reference ] or address specific problems of summarization such as avoiding repeated generations
section : ATTENTION Attentive neural networks have recently demonstrated success in a wide range of tasks ranging from handwriting synthesis [ reference ] , digit classification [ reference ] , machine translation , image captioning [ reference ] , speech recognition [ reference ] and sentence summarization [ reference ] , to geometric reasoning [ reference ] .
But it suffers from the drawback of generation of repeated phrases . In this work we propose a model for the query-based summarization task based on the encode-attend-decode paradigm with two key additions ( i ) a query attention model ( in addition to document attention model ) which learns to focus on different portions of the query at different time steps ( instead of using a static representation for the query ) and ( ii ) a new diversity based attention model which aims to alleviate the problem of repeating phrases in the summary . In order to enable the testing of this model we introduce a new query-based summarization dataset building on debatepedia .
Abstract The rise in the amount of textual resources available on the Internet has created the need for tools of automatic document summarization . The main challenges of query-oriented extractive summarization are ( 1 ) to identify the topics of the documents and ( 2 ) to recover query-relevant sentences of the documents that together cover these topics . Existing graph- or hypergraph-based summarizers use graph-based ranking algorithms to produce individual scores of relevance for the sentences .
Especially , in the case of query-based extractive online forum summarization , curating complex information spread along multiple posts from multiple forum members to create a short meta-summary that answers a given query is a very challenging task . To overcome this challenge , we explore the applicability of microtask crowdsourcing as a fast and cheap alternative for query-based extractive text summarization of online forum discussions . We measure the linguistic quality of crowd-based forum summarizations , which is usually conducted in a traditional laboratory environment with the help of experts , via comparative crowdsourcing and laboratory experiments . To our knowledge , no other study considered query-based extractive text summarization and summary quality evaluation as an application area of the microtask crowdsourcing . By conducting experiments both in crowdsourcing and laboratory environments , and comparing the results of linguistic quality judgments , we found out that microtask crowdsourcing shows high applicability for determining the factors overall quality , grammaticality , non-redundancy , referential clarity , focus , and structure & coherence .
In this paper we use a deep auto-encoder for extractive query-based summarization . We experiment with different input representations in order to overcome the problems stemming from sparse inputs characteristic to linguistic data .
This paper explores the impact of several supervised machine learning approaches for extracting multi-document summaries for given queries . In particular , we compare classification and regression approaches for query-based extractive summarisation using data provided by the BioASQ Challenge . We tackled the problem of annotating sentences for training classification systems and show that a simple annotation approach outperforms regression-based summarisation .
Query-focused summarization laid emphasis on user 's concern in the document . An approach to query-focused automatic summarization is presented . It utilizes search engine as tool for seeking answer .
The most important step of query-focused extractive summarization is deciding which sentences are appropriately included in the final summary . In this paper , we propose a feature fusion based sentence selecting strategy , to identify the sentences with high query-relevance and high information density .
Abstract In the paper , how to meet the users ’ need plays a very important role in the study on the query-oriented automatic summarization . Combining with the influence of text granularities and query information , we construct a model to explain the relationship between text granularities , point out the way based on the semantic relation to test text similarity , adopt a strategy to sentence dynamically allocation to produce summarization .
Query Specific Document Summarization may provide a solution to handle large amount of information present on the World Wide Web . In this TEXT BOOK the work is on query specific extractive text summarization , wherein , sentences are scored based on certain scoring methods and those having high score are extracted to generate summary .
SeeLM17 . HasselqvistHK17 and McCannKXS18 had introduced its RNN - based mechanism to query - based abstractive summarization and question answering , respectively ; however , their models can not handle multiple passages effectively . We extend the mechanism to a Transformer VaswaniSPUJGKP17 based one that allows words to be generated from a fixed vocabulary and words to be copied from both the question and multiple passages .
Extractive Text Summarization Technique Using Fuzzy C-Means Clustering Algorithm Hybrid Approach for Single Text Document Summarization Using Statistical and Sentiment Features Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling Concept generalization and fusion for abstractive sentence generation Compressive Document Summarization via Sparse Optimization Single Document Summarization Based on Triangle Analysis of Dependency Graphs Topical Coherence for Graph-based Extractive Summarization Multi-roles Graph Based Extractive Summarization Classify or Select: Neural Architectures for Extractive Document Summarization A document-sensitive graph model for multi-document summarization Using cohesive properties of text for automatic summarization Query Focused Multi-document Summarization Based on Five-Layered Graph and Universal Paraphrastic Embeddings A Hybrid Approach of Text Summarization Using Latent Semantic Analysis and Deep Learning Graph-Based Keyword Extraction for Single-Document Summarization Sentiment Analysis and Summarization of Twitter Data Sentimental Analysis on Twitter Data using Naive Bayes A Regularized Framework for Sparse and Structured Neural Attention A Similarity-Based Abstract Argumentation Approach to Extractive Text Summarization A topic-based sentence representation for extractive text summarization Anti-serendipity: finding useless documents and similar documents Automatic Text Summarization Implementation of Automatic Text Summarization Text Understanding with a Hybrid Neural Network Based Learning Modeling Novelty and Feature Combination using Support Vector Regression for Update Summarization Abstractive Summarization with the Aid of Extractive Summarization Detection of Topic and its Extrinsic Evaluation Through Multi-Document Summarization A Comparative Study of Sentiment-Based Graphs of Text Summaries Effective LSTMs for Target-Dependent Sentiment Classification Convolutional Sequence to Sequence Learning Reasoning about Entailment with Neural Attention Diversity driven attention model for query-based abstractive summarization Query-oriented text summarization based on hypergraph transversals Crowdsourcing versus the laboratory: Towards crowd-based linguistic text quality assessment of query-based extractive summarization Query-Based Single Document Summarization Using an Ensemble Noisy Auto-Encoder Supervised Machine Learning for Extractive Query Based Summarisation of Biomedical Data Research and implementation of query-focused automatic summarization A New Feature-Fusion Sentence Selecting Strategy for Query-Focused Multi-document Summarization Study on core Technologies of Query-oriented Automatic Summarization Query Specific Multi Document Text Summarization Multi-style Generative Reading Comprehension
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ICLR IJCAI NBiS EMNLP ICONIP (1) CSOC (1) ICACCI CSE AI*IA HICSS ICPCSEE (2) APWeb/WAIM (1) ACL (2) COLING ICML ICLR Qurator ALTA Louhi@EMNLP ALPIT ACL
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