A trainable document summarizer
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
The automatic construction of large-scale corpora for summarization research
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)
Cut-and-paste text summarization
Cut-and-paste text summarization
Aligning sentences in parallel corpora
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
A program for aligning sentences in bilingual corpora
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
Introduction to the special issue on summarization
Computational Linguistics - Summarization
Induction of Word and Phrase Alignments for Automatic Document Summarization
Computational Linguistics
Sentence alignment for monolingual comparable corpora
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Indexing and retrieval of handwritten medical forms
dg.o '07 Proceedings of the 8th annual international conference on Digital government research: bridging disciplines & domains
SlideSeer: a digital library of aligned document and presentation pairs
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
GA, MR, FFNN, PNN and GMM based models for automatic text summarization
Computer Speech and Language
On the subjectivity of human-authored summaries*
Natural Language Engineering
Handwritten document retrieval strategies
Proceedings of The Third Workshop on Analytics for Noisy Unstructured Text Data
Using N-Grams to understand the nature of summaries
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
Using signals of human interest to enhance single-document summarization
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
LexRank: graph-based lexical centrality as salience in text summarization
Journal of Artificial Intelligence Research
Quantifying the limits and success of extractive summarization systems across domains
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Automatic generation of story highlights
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Unsupervised discourse segmentation of documents with inherently parallel structure
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Title generation with quasi-synchronous grammar
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Imposing hierarchical browsing structures onto spoken documents
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
RitroveRAI: a web application for semantic indexing and hyperlinking of multimedia news
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
An unsupervised alignment algorithm for text simplification corpus construction
MTTG '11 Proceedings of the Workshop on Monolingual Text-To-Text Generation
Text summarisation in progress: a literature review
Artificial Intelligence Review
An approach to summarizing Bengali news documents
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
Detecting human features in summaries --- symbol sequence statistical regularity
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
Multiple aspect summarization using integer linear programming
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Text simplification resources for Spanish
Language Resources and Evaluation
Hi-index | 0.00 |
Professional summarizers often reuse original documents to generate summaries. The task of summary sentence decomposition is to deduce whether a summary sentence is constructed by reusing the original text and to identify reused phrases. Specifically, the decomposition program needs to answer three questions for a given summary sentence: (1) Is this summary sentence constructed by reusing the text in the original document? (2) If so, what phrases in the sentence come from the original document? and (3) From where in the document do the phrases come? Solving the decomposition problem can lead to better text generation techniques for summarization. Decomposition can also provide large training and testing corpora for extraction-based summarizers. We propose a hidden Markov model solution to the decomposition problem. Evaluations show that the proposed algorithm performs well.