Update README.md

README.md

@@ -57,7 +57,8 @@ model are the already existing and vastly applied BART-Large CNN, Pegasus-XSUM a
 the Sumy Python Library and include SumyRandom, SumyLuhn, SumyLsa, SumyLexRank, SumyTextRank, SumySumBasic, SumyKL and SumyReduction. Each of the
 methods used for text summarization will be described indvidually in the following sections.
 
-
+| Model | Params |
+|:-----:|:------:|
 
 ![architeru](https://github.com/marcosdib/S2Query/Classification_Architecture_model.png)
 
@@ -327,17 +328,17 @@ Table 2: Results from Pre-trained Longformer + ML models.
 
 ```bibtex
 @conference{webist22,
-author       ={Carlos Rocha. and Marcos Dib. and Li Weigang. and Andrea Nunes. and Allan Faria. and Daniel Cajueiro.
-               and Maísa {Kely de Melo}. and Victor Celestino.},
-title        ={Using Transfer Learning To Classify Long Unstructured Texts with Small Amounts of Labeled Data},
+author       ={Daniel O. Cajueiro and Maísa {Kely de Melo}. and Arthur G. Nery and Silvia A. dos Reis and Igor Tavares
+              and Li Weigang and Victor R. R. Celestino.},
+title        ={A comprehensive review of automatic text summarization techniques: method, data, evaluation and coding},
 booktitle    ={Proceedings of the 18th International Conference on Web Information Systems and Technologies - WEBIST,},
 year         ={2022},
-pages        ={201-213},
-publisher    ={SciTePress},
-organization ={INSTICC},
-doi          ={10.5220/0011527700003318},
-isbn         ={978-989-758-613-2},
-issn         ={2184-3252},
+pages        ={},
+publisher    ={},
+organization ={},
+doi          ={},
+isbn         ={},
+issn         ={},
 }
 ```