A Proposition-based Abstractive Summarizer

Conference Object
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Fang, Y 
Zhu, H 
Muszynska, E 
Teufel, SH 

Abstractive summarisation is not yet common amongst today's deployed and research systems. Most existing systems either extract sentences or compress individual sentences. In this paper, we present a summariser that works by a different paradigm. It is a further development of an existing summariser that has an incremental, proposition-based content selection process but lacks a natural language (NL) generator for the final output. Using an NL generator, we can now produce the summary text to directly reflect the selected propositions. Our evaluation compares textual quality of our system to the earlier preliminary output method, and also uses ROUGE to compare to various summarisers that use the traditional method of sentence extraction, followed by compression. Our results suggest that cutting out the middleman of sentence extraction can lead to better abstractive summaries.

Journal Title
Proceedings of COLING 2016
Conference Name
COLING 2016: The 26th International Conference on Computational Linguistics
Journal ISSN
Volume Title
International Committee on Computational Linguistics