We consider the problem of query-focused multidocument summarization, where a summary containing the information most relevant to a user's information need is produced from a set of topic-related documents. We propose a new method based on probabilistic latent semantic analysis, which allows us to represent sentences and queries as probability distributions over latent topics. Our approach combines queryfocused and thematic features computed in the latent topic space to estimate the summaryrelevance of sentences. In addition, we evaluate several different similarity measures for computing sentence-level feature scores. Experimental results show that our approach outperforms the best reported results on DUC 2006 data, and also compares well on DUC 2007 data.
REF computes the similarity of sentences with a user-defined query based on PLSA.
Topic-based Multi-Document Summarization with Probabilistic Latent Semantic Analysis
RANLP
Computer Science