TAILIEUCHUNG - Báo cáo khoa học: "Query Snowball: A Co-occurrence-based Approach to Multi-document Summarization for Question Answering"

We propose a new method for query-oriented extractive multi-document summarization. To enrich the information need representation of a given query, we build a co-occurrence graph to obtain words that augment the original query terms. We then formulate the summarization problem as a Maximum Coverage Problem with Knapsack Constraints based on word pairs rather than single words. | Query Snowball A Co-occurrence-based Approach to Multi-document Summarization for Question Answering Hajime Morita12 and Tetsuya Sakai1 and Manabu Okumura3 1 Microsoft Research Asia Beijing China 2Tokyo Institute of Technology Tokyo Japan 3Precision and Intelligence Laboratory Tokyo Institute of Technology Tokyo Japan morita@ tetsuyasakai@ oku@ Abstract We propose a new method for query-oriented extractive multi-document summarization. To enrich the information need representation of a given query we build a co-occurrence graph to obtain words that augment the original query terms. We then formulate the summarization problem as a Maximum Coverage Problem with Knapsack Constraints based on word pairs rather than single words. Our experiments with the NTCIR ACLIA question answering test collections show that our method achieves a pyramid F3-score of up to a 36 improvement over a baseline using Maximal Marginal Relevance. 1 Introduction Automatic text summarization aims at reducing the amount of text the user has to read while preserving important contents and has many applications in this age of digital information overload Mani 2001 . In particular query-oriented multi-document summarization is useful for helping the user satisfy his information need efficiently by gathering important pieces of information from multiple documents. In this study we focus on extractive summarization Liu and Liu 2009 in particular on sentence selection from a given set of source documents that contain relevant sentences. One well-known challenge in selecting sentences relevant to the information need is the vocabulary mismatch between the query . information need representation and the candidate sentences. Hence to enrich the information need representation we build a co-occurrence 223 graph to obtain words that augment the original query terms. We call this method Query Snowball. Another challenge in sentence selection for .

• Báo cáo khoa học
• A Co occurrence based Approach
• Multi document Summarization
• Question Answering
• báo cáo khoa học
• báo cáo ngôn ngữ
• ngôn ngữ tự nhiên