Foundations of statistical natural language processing
Foundations of statistical natural language processing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
New Methods in Automatic Extracting
Journal of the ACM (JACM)
Statistics-Based Summarization - Step One: Sentence Compression
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Sentence level discourse parsing using syntactic and lexical information
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Improving summarization performance by sentence compression: a pilot study
AsianIR '03 Proceedings of the sixth international workshop on Information retrieval with Asian languages - Volume 11
Discourse chunking and its application to sentence compression
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
A syntactic and lexical-based discourse segmenter
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
A new hybrid summarizer based on vector space model, statistical physics and linguistics
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Discourse segmentation for Spanish based on shallow parsing
MICAI'10 Proceedings of the 9th Mexican international conference on Advances in artificial intelligence: Part I
Discourse segmentation for sentence compression
MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
Hi-index | 0.00 |
This paper presents a method for automatic summarization by deleting intra-sentence discourse segments. First, each sentence is divided into elementary discourse units and, then, less informative segments are deleted. To analyze the results, we have set up an annotation campaign, thanks to which we have found interesting aspects regarding the elimination of discourse segments as an alternative to sentence compression task. Results show that the degree of disagreement in determining the optimal compressed sentence is high and increases with the complexity of the sentence. However, there is some agreement on the decision to delete discourse segments. The informativeness of each segment is calculated using textual energy, a method that has shown good results in automatic summarization.