The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Retrieval and novelty detection at the sentence level
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Understanding user goals in web search
Proceedings of the 13th international conference on World Wide Web
Producing biographical summaries: combining linguistic knowledge with corpus statistics
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Offline strategies for online question answering: answering questions before they are asked
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Graph-based ranking algorithms for sentence extraction, applied to text summarization
ACLdemo '04 Proceedings of the ACL 2004 on Interactive poster and demonstration sessions
Information extraction for question answering: improving recall through syntactic patterns
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Novelty detection: the TREC experience
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Descriptive question answering in encyclopedia
ACLdemo '05 Proceedings of the ACL 2005 on Interactive poster and demonstration sessions
LexRank: graph-based lexical centrality as salience in text summarization
Journal of Artificial Intelligence Research
Dublin city university at QA@CLEF 2008
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
Hi-index | 0.00 |
We present a transparent model for ranking sentences that incorporates topic relevance as well as an aboutness and importance feature. We describe and compare five methods for estimating the importance feature. The two key features that we use are graph-based ranking and ranking based on reference corpora of sentences known to be important. Independently those features do not improve over the baseline, but combined they do. While our experimental evaluation focuses on informational queries about people, our importance estimation methods are completely general and can be applied to any topic.