@inproceedings{ernst-etal-2021-summary,
title = "Summary-Source Proposition-level Alignment: Task, Datasets and Supervised Baseline",
author = "Ernst, Ori and
Shapira, Ori and
Pasunuru, Ramakanth and
Lepioshkin, Michael and
Goldberger, Jacob and
Bansal, Mohit and
Dagan, Ido",
booktitle = "Proceedings of the 25th Conference on Computational Natural Language Learning",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.conll-1.25",
doi = "10.18653/v1/2021.conll-1.25",
pages = "310--322",
abstract = "Aligning sentences in a reference summary with their counterparts in source documents was shown as a useful auxiliary summarization task, notably for generating training data for salience detection. Despite its assessed utility, the alignment step was mostly approached with heuristic unsupervised methods, typically ROUGE-based, and was never independently optimized or evaluated. In this paper, we propose establishing summary-source alignment as an explicit task, while introducing two major novelties: (1) applying it at the more accurate proposition span level, and (2) approaching it as a supervised classification task. To that end, we created a novel training dataset for proposition-level alignment, derived automatically from available summarization evaluation data. In addition, we crowdsourced dev and test datasets, enabling model development and proper evaluation. Utilizing these data, we present a supervised proposition alignment baseline model, showing improved alignment-quality over the unsupervised approach.",
}
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<abstract>Aligning sentences in a reference summary with their counterparts in source documents was shown as a useful auxiliary summarization task, notably for generating training data for salience detection. Despite its assessed utility, the alignment step was mostly approached with heuristic unsupervised methods, typically ROUGE-based, and was never independently optimized or evaluated. In this paper, we propose establishing summary-source alignment as an explicit task, while introducing two major novelties: (1) applying it at the more accurate proposition span level, and (2) approaching it as a supervised classification task. To that end, we created a novel training dataset for proposition-level alignment, derived automatically from available summarization evaluation data. In addition, we crowdsourced dev and test datasets, enabling model development and proper evaluation. Utilizing these data, we present a supervised proposition alignment baseline model, showing improved alignment-quality over the unsupervised approach.</abstract>
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%0 Conference Proceedings
%T Summary-Source Proposition-level Alignment: Task, Datasets and Supervised Baseline
%A Ernst, Ori
%A Shapira, Ori
%A Pasunuru, Ramakanth
%A Lepioshkin, Michael
%A Goldberger, Jacob
%A Bansal, Mohit
%A Dagan, Ido
%S Proceedings of the 25th Conference on Computational Natural Language Learning
%D 2021
%8 nov
%I Association for Computational Linguistics
%C Online
%F ernst-etal-2021-summary
%X Aligning sentences in a reference summary with their counterparts in source documents was shown as a useful auxiliary summarization task, notably for generating training data for salience detection. Despite its assessed utility, the alignment step was mostly approached with heuristic unsupervised methods, typically ROUGE-based, and was never independently optimized or evaluated. In this paper, we propose establishing summary-source alignment as an explicit task, while introducing two major novelties: (1) applying it at the more accurate proposition span level, and (2) approaching it as a supervised classification task. To that end, we created a novel training dataset for proposition-level alignment, derived automatically from available summarization evaluation data. In addition, we crowdsourced dev and test datasets, enabling model development and proper evaluation. Utilizing these data, we present a supervised proposition alignment baseline model, showing improved alignment-quality over the unsupervised approach.
%R 10.18653/v1/2021.conll-1.25
%U https://aclanthology.org/2021.conll-1.25
%U https://doi.org/10.18653/v1/2021.conll-1.25
%P 310-322
Markdown (Informal)
[Summary-Source Proposition-level Alignment: Task, Datasets and Supervised Baseline](https://aclanthology.org/2021.conll-1.25) (Ernst et al., CoNLL 2021)
ACL