Using Syntactic and Shallow Semantic Kernels to Improve Multi-Modality Manifold-Ranking for Topic-Focused Multi-Document Summarization
Yllias Chali, Sadid A. Hasan, Kaisar Imam · 2011
Multi-modality manifold-ranking is re-cently used successfully in topic-focused multi-document summarization. This ap-proach is based on Bag-Of-Words (BOW) assumption where the pair-wise similar-ity values between sentences are computed using the standard cosine similarity mea-sure (TF*IDF). However, the major lim-itation 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 ker-nels for computing the relevance between the sentences. We argue that the addi-tion of syntactic and semantic information can improve the performance of the multi-modality manifold-ranking algorithm. Ex-tensive experiments on the DUC bench-mark datasets prove the effectiveness of our approach. 1