Measuring Importance and Query Relevance in Topic-focused Multi-document Summarization

The increasing complexity of summarization systems makes it difficult to analyze exactly which modules make a difference in performance. We carried out a principled comparison between the two most commonly used schemes for assigning importance to words in the context of query focused multi-document summarization: raw frequency (word probability) and log-likelihood ratio. We demonstrate that the advantages of log-likelihood ratio come from its known distributional properties which allow for the identification of a set of words that in its entirety defines the aboutness of the input. We also find that LLR is more suitable for query-focused summarization since, unlike raw frequency, it is more sensitive to the integration of the information need defined by the user.

• Type: Conference
• Year: 2007
• Where: ACL
• Authors: Surabhi Gupta, Ani Nenkova, Daniel Jurafsky
• Keywords: ACL 2007, Computational Linguistics, Log-likelihood Ratio, Multi-document Summarization, Summarization Systems