Capturing Sentence Prior for Query-Based Multi-Document Summarization

In this paper, we have considered a real world information synthesis task, generation of a fixed length multi document summary which satisfies a specific information need. This task was mapped to a topic-oriented, informative multi-document summarization. We also tried to estimate, given the human written reference summaries and the document set, the maximum performance (ROUGE1 scores) that can be achieved by an extraction-based summarization technique. Motivated by the observation that the current approaches are far behind the estimated maximum performance, we have looked at Information Retrieval techniques to improve the relevance scoring of sentences towards information need. Following information theoretic approach we have identified a measure to capture the notion of importance or prior of a sentence. Following a different decomposition of Probability Ranking Principle, the calculated importance/prior is incorporated into the final sentence scoring by weighted linear combina...

• Type: Conference
• Year: 2007
• Where: RIAO
• Authors: Jagadeesh Jagarlamudi, Prasad Pingali, Vasudeva Varma
• Keywords: Information Need, Information Technology, Informative Multi-document Summarization, Maximum Performance, RIAO 2007