Summarizing text documents: sentence selection and evaluation metrics
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
SUMMAC: a text summarization evaluation
Natural Language Engineering
On the reliability of factoid question answering evaluation
ACM Transactions on Asian Language Information Processing (TALIP)
Semantic Retrieval of Text Documents
CIT '07 Proceedings of the 7th IEEE International Conference on Computer and Information Technology
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Monolingual web-based factoid question answering in Chinese, Swedish, English and Japanese
MLQA '06 Proceedings of the Workshop on Multilingual Question Answering
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The Internet is a major source of information for the end users. General purpose search engines are very advanced, but finding appropriate information on the Internet is still difficult. From the end user point point of view, there are two key reasons for that. One is that the queries must be expressed in an artificial form such as a set of key words. The other is that the search results display only snippets containing the search terms. These snippets, however, are insufficient for determining result relevance as they do not really summarize the content of the document they represent. Search engines should thus generate indicative summaries that help the user understand the content of documents without downloading them. In this paper, we propose an approach that selects the most important sentences semantically relevant to the user query and derives the paragraphs including them. This approach is applicable to the Japanese language. Our experiments show the promizing results.