Extractive Summarization under Strict Length Constraints

Yashar Mehdad, Amanda Stent, Kapil Thadani, Dragomir Radev, Youssef Billawala, Karolina Buchner


Abstract
In this paper we report a comparison of various techniques for single-document extractive summarization under strict length budgets, which is a common commercial use case (e.g. summarization of news articles by news aggregators). We show that, evaluated using ROUGE, numerous algorithms from the literature fail to beat a simple lead-based baseline for this task. However, a supervised approach with lightweight and efficient features improves over the lead-based baseline. Additional human evaluation demonstrates that the supervised approach also performs competitively with a commercial system that uses more sophisticated features.
Anthology ID:
L16-1493
Volume:
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
Month:
May
Year:
2016
Address:
Portorož, Slovenia
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
3089–3093
Language:
URL:
https://www.aclweb.org/anthology/L16-1493
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/L16-1493.pdf