Enhancing Biomedical Scientific Reviews Summarization with Graph-based Factual Evidence Extracted from Papers

Giacomo Frisoni, Paolo Italiani, Francesco Boschi, Gianluca Moro

2022

Abstract

Combining structured knowledge and neural language models to tackle natural language processing tasks is a recent research trend that catalyzes community attention. This integration holds a lot of potential in document summarization, especially in the biomedical domain, where the jargon and the complex facts make the overarching information truly hard to interpret. In this context, graph construction via semantic parsing plays a crucial role in unambiguously capturing the most relevant parts of a document. However, current works are limited to extracting open-domain triples, failing to model real-world n-ary and nested biomedical interactions accurately. To alleviate this issue, we present EASumm, the first framework for biomedical abstractive summarization enhanced by event graph extraction (i.e., graphical representations of medical evidence learned from scientific text), relying on dual text-graph encoders. Extensive evaluations on the CDSR dataset corroborate the importance of explicit event structures, with better or comparable performance than previous state-of-the-art systems. Finally, we offer some hints to guide future research in the field.

Paper Citation

in Harvard Style

Frisoni G., Italiani P., Boschi F. and Moro G. (2022). Enhancing Biomedical Scientific Reviews Summarization with Graph-based Factual Evidence Extracted from Papers. In Proceedings of the 11th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-583-8, pages 168-179. DOI: 10.5220/0011354900003269

in Bibtex Style

@conference{data22,
author={Giacomo Frisoni and Paolo Italiani and Francesco Boschi and Gianluca Moro},
title={Enhancing Biomedical Scientific Reviews Summarization with Graph-based Factual Evidence Extracted from Papers},
booktitle={Proceedings of the 11th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2022},
pages={168-179},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011354900003269},
isbn={978-989-758-583-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Enhancing Biomedical Scientific Reviews Summarization with Graph-based Factual Evidence Extracted from Papers
SN - 978-989-758-583-8
AU - Frisoni G.
AU - Italiani P.
AU - Boschi F.
AU - Moro G.
PY - 2022
SP - 168
EP - 179
DO - 10.5220/0011354900003269