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    <abstract>Due to their length and complexity, long regulatory texts are challenging to summarize. To address this, a multi-step extractive-abstractive architecture is proposed to handle lengthy regulatory documents more effectively. In this paper, we show that the effectiveness of a two-step architecture for summarizing long regulatory texts varies significantly depending on the model used. Specifically, the two-step architecture improves the performance of decoder-only models. For abstractive encoder-decoder models with short context lengths, the effectiveness of an extractive step varies, whereas for long-context encoder-decoder models, the extractive step worsens their performance. This research also highlights the challenges of evaluating generated texts, as evidenced by the differing results from human and automated evaluations. Most notably, human evaluations favoured language models pretrained on legal text, while automated metrics rank general-purpose language models higher. The results underscore the importance of selecting the appropriate summarization strategy based on model architecture and context length.</abstract>
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%0 Conference Proceedings
%T Summarizing Long Regulatory Documents with a Multi-Step Pipeline
%A Sie, Mika
%A Beek, Ruby
%A Bots, Michiel
%A Brinkkemper, Sjaak
%A Gatt, Albert
%Y Aletras, Nikolaos
%Y Chalkidis, Ilias
%Y Barrett, Leslie
%Y Goan\textcommabelowtă, Cătălina
%Y Preo\textcommabelowtiuc-Pietro, Daniel
%Y Spanakis, Gerasimos
%S Proceedings of the Natural Legal Language Processing Workshop 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, FL, USA
%F sie-etal-2024-summarizing
%X Due to their length and complexity, long regulatory texts are challenging to summarize. To address this, a multi-step extractive-abstractive architecture is proposed to handle lengthy regulatory documents more effectively. In this paper, we show that the effectiveness of a two-step architecture for summarizing long regulatory texts varies significantly depending on the model used. Specifically, the two-step architecture improves the performance of decoder-only models. For abstractive encoder-decoder models with short context lengths, the effectiveness of an extractive step varies, whereas for long-context encoder-decoder models, the extractive step worsens their performance. This research also highlights the challenges of evaluating generated texts, as evidenced by the differing results from human and automated evaluations. Most notably, human evaluations favoured language models pretrained on legal text, while automated metrics rank general-purpose language models higher. The results underscore the importance of selecting the appropriate summarization strategy based on model architecture and context length.
%U https://aclanthology.org/2024.nllp-1.2
%P 18-32

Markdown (Informal)

[Summarizing Long Regulatory Documents with a Multi-Step Pipeline](https://aclanthology.org/2024.nllp-1.2) (Sie et al., NLLP 2024)

• Summarizing Long Regulatory Documents with a Multi-Step Pipeline (Sie et al., NLLP 2024)

ACL

• Mika Sie, Ruby Beek, Michiel Bots, Sjaak Brinkkemper, and Albert Gatt. 2024. Summarizing Long Regulatory Documents with a Multi-Step Pipeline. In Proceedings of the Natural Legal Language Processing Workshop 2024, pages 18–32, Miami, FL, USA. Association for Computational Linguistics.