Using Syntactic and Shallow Semantic Kernels to Improve Multi-Modality Manifold-Ranking for Topic-Focused Multi-Document Summarization
Multi-modality manifold-ranking is recently used successfully in topic-focused multi-document summarization. This approach is based on Bag-Of-Words (BOW) assumption where the pair-wise similarity values between sentences are computed using the standard cosine similarity measure (TF*IDF). However, the major limitation of the TF*IDF approach is that it only retains the frequency of the words and disregards the syntactic and semantic information. In this paper, we propose the use of syntactic and shallow semantic kernels for computing the relevance between the sentences. We argue that the addition of syntactic and semantic information can improve the performance of the multimodality manifold-ranking algorithm. Extensive experiments on the DUC benchmark datasets prove the effectiveness of our approach.