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
Motivations and methods for text simplification
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
Centroid-based summarization of multiple documents
Information Processing and Management: an International Journal
Sentence Fusion for Multidocument News Summarization
Computational Linguistics
Syntactic simplification for improving content selection in multi-document summarization
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
The Pyramid Method: Incorporating human content selection variation in summarization evaluation
ACM Transactions on Speech and Language Processing (TSLP)
SimpleNLG: a realisation engine for practical applications
ENLG '09 Proceedings of the 12th European Workshop on Natural Language Generation
Sentence compression as tree transduction
Journal of Artificial Intelligence Research
Syntax-driven sentence revision for broadcast news summarization
UCNLG+Sum '09 Proceedings of the 2009 Workshop on Language Generation and Summarisation
A survey of paraphrasing and textual entailment methods
Journal of Artificial Intelligence Research
Fully abstractive approach to guided summarization
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
Application of Text Summarization techniques to the Geographical Information Retrieval task
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
We propose a new, ambitious framework for abstractive summarization, which aims at selecting the content of a summary not from sentences, but from an abstract representation of the source documents. This abstract representation relies on the concept of Information Items (InIt), which we define as the smallest element of coherent information in a text or a sentence. Our framework differs from previous abstractive summarization models in requiring a semantic analysis of the text. We present a first attempt made at developing a system from this framework, along with evaluation results for it from TAC 2010. We also present related work, both from within and outside of the automatic summarization domain.