@inproceedings{levy-etal-2016-modeling,
title = "Modeling Extractive Sentence Intersection via Subtree Entailment",
author = "Levy, Omer and
Dagan, Ido and
Stanovsky, Gabriel and
Eckle-Kohler, Judith and
Gurevych, Iryna",
editor = "Matsumoto, Yuji and
Prasad, Rashmi",
booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://aclanthology.org/C16-1272",
pages = "2891--2901",
abstract = "Sentence intersection captures the semantic overlap of two texts, generalizing over paradigms such as textual entailment and semantic text similarity. Despite its modeling power, it has received little attention because it is difficult for non-experts to annotate. We analyze 200 pairs of similar sentences and identify several underlying properties of sentence intersection. We leverage these insights to design an algorithm that decomposes the sentence intersection task into several simpler annotation tasks, facilitating the construction of a high quality dataset via crowdsourcing. We implement this approach and provide an annotated dataset of 1,764 sentence intersections.",
}
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<abstract>Sentence intersection captures the semantic overlap of two texts, generalizing over paradigms such as textual entailment and semantic text similarity. Despite its modeling power, it has received little attention because it is difficult for non-experts to annotate. We analyze 200 pairs of similar sentences and identify several underlying properties of sentence intersection. We leverage these insights to design an algorithm that decomposes the sentence intersection task into several simpler annotation tasks, facilitating the construction of a high quality dataset via crowdsourcing. We implement this approach and provide an annotated dataset of 1,764 sentence intersections.</abstract>
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%0 Conference Proceedings
%T Modeling Extractive Sentence Intersection via Subtree Entailment
%A Levy, Omer
%A Dagan, Ido
%A Stanovsky, Gabriel
%A Eckle-Kohler, Judith
%A Gurevych, Iryna
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F levy-etal-2016-modeling
%X Sentence intersection captures the semantic overlap of two texts, generalizing over paradigms such as textual entailment and semantic text similarity. Despite its modeling power, it has received little attention because it is difficult for non-experts to annotate. We analyze 200 pairs of similar sentences and identify several underlying properties of sentence intersection. We leverage these insights to design an algorithm that decomposes the sentence intersection task into several simpler annotation tasks, facilitating the construction of a high quality dataset via crowdsourcing. We implement this approach and provide an annotated dataset of 1,764 sentence intersections.
%U https://aclanthology.org/C16-1272
%P 2891-2901
Markdown (Informal)
[Modeling Extractive Sentence Intersection via Subtree Entailment](https://aclanthology.org/C16-1272) (Levy et al., COLING 2016)
ACL
- Omer Levy, Ido Dagan, Gabriel Stanovsky, Judith Eckle-Kohler, and Iryna Gurevych. 2016. Modeling Extractive Sentence Intersection via Subtree Entailment. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2891–2901, Osaka, Japan. The COLING 2016 Organizing Committee.