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    <abstract>Automatic text summarization helps to digest the vast and ever-growing amount of scientific publications. While transformer-based solutions like BERT and SciBERT have advanced scientific summarization, lengthy documents pose a challenge due to the token limits of these models. To address this issue, we introduce and evaluate a two-stage model that combines an extract-then-compress framework. Our model incorporates a “graph-augmented extraction module” to select order-based salient sentences and an “abstractive compression module” to generate concise summaries. Additionally, we introduce the *BioConSumm* dataset, which focuses on biodiversity conservation, to support underrepresented domains and explore domain-specific summarization strategies. Out of the tested models, our model achieves the highest ROUGE-2 and ROUGE-L scores on our newly created dataset (*BioConSumm*) and on the *SUMPUBMED* dataset, which serves as a benchmark in the field of biomedicine.</abstract>
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%0 Conference Proceedings
%T Two-Stage Graph-Augmented Summarization of Scientific Documents
%A Rezapour, Rezvaneh
%A Ge, Yubin
%A Han, Kanyao
%A Jeong, Ray
%A Diesner, Jana
%Y Peled-Cohen, Lotem
%Y Calderon, Nitay
%Y Lissak, Shir
%Y Reichart, Roi
%S Proceedings of the 1st Workshop on NLP for Science (NLP4Science)
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, FL, USA
%F rezapour-etal-2024-two
%X Automatic text summarization helps to digest the vast and ever-growing amount of scientific publications. While transformer-based solutions like BERT and SciBERT have advanced scientific summarization, lengthy documents pose a challenge due to the token limits of these models. To address this issue, we introduce and evaluate a two-stage model that combines an extract-then-compress framework. Our model incorporates a “graph-augmented extraction module” to select order-based salient sentences and an “abstractive compression module” to generate concise summaries. Additionally, we introduce the *BioConSumm* dataset, which focuses on biodiversity conservation, to support underrepresented domains and explore domain-specific summarization strategies. Out of the tested models, our model achieves the highest ROUGE-2 and ROUGE-L scores on our newly created dataset (*BioConSumm*) and on the *SUMPUBMED* dataset, which serves as a benchmark in the field of biomedicine.
%U https://aclanthology.org/2024.nlp4science-1.5
%P 36-46

Markdown (Informal)

[Two-Stage Graph-Augmented Summarization of Scientific Documents](https://aclanthology.org/2024.nlp4science-1.5) (Rezapour et al., NLP4Science 2024)

• Two-Stage Graph-Augmented Summarization of Scientific Documents (Rezapour et al., NLP4Science 2024)

ACL

• Rezvaneh Rezapour, Yubin Ge, Kanyao Han, Ray Jeong, and Jana Diesner. 2024. Two-Stage Graph-Augmented Summarization of Scientific Documents. In Proceedings of the 1st Workshop on NLP for Science (NLP4Science), pages 36–46, Miami, FL, USA. Association for Computational Linguistics.