Text-to-Text Extraction and Verbalization of Biomedical Event Graphs

Giacomo Frisoni, Gianluca Moro, Lorenzo Balzani

Abstract
Biomedical events represent complex, graphical, and semantically rich interactions expressed in the scientific literature. Almost all contributions in the event realm orbit around semantic parsing, usually employing discriminative architectures and cumbersome multi-step pipelines limited to a small number of target interaction types. We present the first lightweight framework to solve both event extraction and event verbalization with a unified text-to-text approach, allowing us to fuse all the resources so far designed for different tasks. To this end, we present a new event graph linearization technique and release highly comprehensive event-text paired datasets, covering more than 150 event types from multiple biology subareas (English language). By streamlining parsing and generation to translations, we propose baseline transformer model results according to multiple biomedical text mining benchmarks and NLG metrics. Our extractive models achieve greater state-of-the-art performance than single-task competitors and show promising capabilities for the controlled generation of coherent natural language utterances from structured data.
Anthology ID:
2022.coling-1.238
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
2692–2710
Language:
URL:
https://aclanthology.org/2022.coling-1.238
DOI:
Bibkey:
Cite (ACL):
Giacomo Frisoni, Gianluca Moro, and Lorenzo Balzani. 2022. Text-to-Text Extraction and Verbalization of Biomedical Event Graphs. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2692–2710, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Text-to-Text Extraction and Verbalization of Biomedical Event Graphs (Frisoni et al., COLING 2022)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.coling-1.238.pdf
Code
 disi-unibo-nlp/bio-ee-egv
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