A Relevance - Based Language Modeling approach to DUC 2005 ∗

The task in Document Understanding Conferences (DUC) 2005 is to generate fixed length, user oriented, multi document summary. Our approach to address this task is primarily motivated by the observation that metrics based on key concepts overlap give better results when compared to metrics based on n-gram and sentence overlap. In this paper, we present a sentence extraction based summarization system which scores the sentences using Relevance Based Language Modeling, Latent Semantic Indexing and number of special words. From these scored sentences, the system generates a summary of required granularity. Our summarization system was ranked 3,4,8 and 17 in ROUGE-SU4, ROUGE-2, responsiveness and linguistic quality evaluations respectively. In post DUC analysis we found that LSI has negative effect on the systems performance, and the performance gained by 5.4% when it is implemented using language modeling and number of special words.