@inproceedings{mehdad-etal-2016-extractive,
title = "Extractive Summarization under Strict Length Constraints",
author = "Mehdad, Yashar and
Stent, Amanda and
Thadani, Kapil and
Radev, Dragomir and
Billawala, Youssef and
Buchner, Karolina",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Goggi, Sara and
Grobelnik, Marko and
Maegaard, Bente and
Mariani, Joseph and
Mazo, Helene and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
month = may,
year = "2016",
address = "Portoro{\v{z}}, Slovenia",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/L16-1493",
pages = "3089--3093",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Extractive Summarization under Strict Length Constraints
%A Mehdad, Yashar
%A Stent, Amanda
%A Thadani, Kapil
%A Radev, Dragomir
%A Billawala, Youssef
%A Buchner, Karolina
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Grobelnik, Marko
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Helene
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16)
%D 2016
%8 May
%I European Language Resources Association (ELRA)
%C Portorož, Slovenia
%F mehdad-etal-2016-extractive
%X 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.
%U https://aclanthology.org/L16-1493
%P 3089-3093
Markdown (Informal)
[Extractive Summarization under Strict Length Constraints](https://aclanthology.org/L16-1493) (Mehdad et al., LREC 2016)
ACL
- Yashar Mehdad, Amanda Stent, Kapil Thadani, Dragomir Radev, Youssef Billawala, and Karolina Buchner. 2016. Extractive Summarization under Strict Length Constraints. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 3089–3093, Portorož, Slovenia. European Language Resources Association (ELRA).