Generic text summarization using relevance measure and latent semantic analysis
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
An empirically based system for processing definite descriptions
Computational Linguistics
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Robust pronoun resolution with limited knowledge
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
CAST: a computer-aided summarisation tool
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 2
Centering: A Parametric Theory and Its Instantiations
Computational Linguistics
NAACL-ANLP-AutoSum '00 Proceedings of the 2000 NAACL-ANLPWorkshop on Automatic summarization - Volume 4
Automated text summarization and the SUMMARIST system
TIPSTER '98 Proceedings of a workshop on held at Baltimore, Maryland: October 13-15, 1998
Using coreference chains for text summarization
CorefApp '99 Proceedings of the Workshop on Coreference and its Applications
Text summarization and singular value decomposition
ADVIS'04 Proceedings of the Third international conference on Advances in Information Systems
Two uses of anaphora resolution in summarization
Information Processing and Management: an International Journal
Designing an interactive open-domain question answering system
Natural Language Engineering
Wordica: Emergence of linguistic representations for words by independent component analysis
Natural Language Engineering
Identifying novel information using latent semantic analysis in the WiQA task at CLEF 2006
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
| Hi-index | 0.01 |
We propose an approach to summarization exploiting both lexical information and the output of an automatic anaphoric resolver, and using Singular Value Decomposition (SVD) to identify the main terms. We demonstrate that adding anaphoric information results in significant performance improvements over a previously developed system, in which only lexical terms are used as the input to SVD. However, we also show that how anaphoric information is used is crucial: whereas using this information to add new terms does result in improved performance, simple substitution makes the performance worse.