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Two-Stage Graph-Augmented Summarization of Scientific Documents
Conference proceeding   Open access

Two-Stage Graph-Augmented Summarization of Scientific Documents

Rezvaneh Rezapour, Yubin Ge, Kanyao Han, Ray Jeong and Jana Diesner
NLP4Science 2024 - 1st Workshop on NLP for Science, Proceedings of the Workshop, pp 36-46
2024
DOI: https://doi.org/10.18653/v1/2024.nlp4science-1.5
url
https://doi.org/10.18653/v1/2024.nlp4science-1.5View
Published, Version of Record (VoR) Open

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.

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