@inproceedings{kamezawa-etal-2022-rnsum,
title = "{RNS}um: A Large-Scale Dataset for Automatic Release Note Generation via Commit Logs Summarization",
author = "Kamezawa, Hisashi and
Nishida, Noriki and
Shimizu, Nobuyuki and
Miyazaki, Takashi and
Nakayama, Hideki",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.597",
doi = "10.18653/v1/2022.acl-long.597",
pages = "8718--8735",
abstract = "A release note is a technical document that describes the latest changes to a software product and is crucial in open source software development. However, it still remains challenging to generate release notes automatically. In this paper, we present a new dataset called RNSum, which contains approximately 82,000 English release notes and the associated commit messages derived from the online repositories in GitHub. Then, we propose classwise extractive-then-abstractive/abstractive summarization approaches to this task, which can employ a modern transformer-based seq2seq network like BART and can be applied to various repositories without specific constraints. The experimental results on the RNSum dataset show that the proposed methods can generate less noisy release notes at higher coverage than the baselines. We also observe that there is a significant gap in the coverage of essential information when compared to human references. Our dataset and the code are publicly available.",
}
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<abstract>A release note is a technical document that describes the latest changes to a software product and is crucial in open source software development. However, it still remains challenging to generate release notes automatically. In this paper, we present a new dataset called RNSum, which contains approximately 82,000 English release notes and the associated commit messages derived from the online repositories in GitHub. Then, we propose classwise extractive-then-abstractive/abstractive summarization approaches to this task, which can employ a modern transformer-based seq2seq network like BART and can be applied to various repositories without specific constraints. The experimental results on the RNSum dataset show that the proposed methods can generate less noisy release notes at higher coverage than the baselines. We also observe that there is a significant gap in the coverage of essential information when compared to human references. Our dataset and the code are publicly available.</abstract>
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%0 Conference Proceedings
%T RNSum: A Large-Scale Dataset for Automatic Release Note Generation via Commit Logs Summarization
%A Kamezawa, Hisashi
%A Nishida, Noriki
%A Shimizu, Nobuyuki
%A Miyazaki, Takashi
%A Nakayama, Hideki
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F kamezawa-etal-2022-rnsum
%X A release note is a technical document that describes the latest changes to a software product and is crucial in open source software development. However, it still remains challenging to generate release notes automatically. In this paper, we present a new dataset called RNSum, which contains approximately 82,000 English release notes and the associated commit messages derived from the online repositories in GitHub. Then, we propose classwise extractive-then-abstractive/abstractive summarization approaches to this task, which can employ a modern transformer-based seq2seq network like BART and can be applied to various repositories without specific constraints. The experimental results on the RNSum dataset show that the proposed methods can generate less noisy release notes at higher coverage than the baselines. We also observe that there is a significant gap in the coverage of essential information when compared to human references. Our dataset and the code are publicly available.
%R 10.18653/v1/2022.acl-long.597
%U https://aclanthology.org/2022.acl-long.597
%U https://doi.org/10.18653/v1/2022.acl-long.597
%P 8718-8735
Markdown (Informal)
[RNSum: A Large-Scale Dataset for Automatic Release Note Generation via Commit Logs Summarization](https://aclanthology.org/2022.acl-long.597) (Kamezawa et al., ACL 2022)
ACL