Pre-Gamus: Reducing Complexity of Scientific Literature as a Support against Misinformation

Nico Colic, Jin-Dong Kim, Fabio Rinaldi


Abstract
Scientific literature encodes a wealth of knowledge relevant to various users. However, the complexity of scientific jargon makes it inaccessible to all but domain specialists. It would be helpful for different types of people to be able to get at least a gist of a paper. Biomedical practitioners often find it difficult to keep up with the information load; but even lay people would benefit from scientific information, for example to dispel medical misconceptions. Besides, in many countries, familiarity with English is limited, let alone scientific English, even among professionals. All this points to the need for simplified access to the scientific literature. We thus present an application aimed at solving this problem, which is capable of summarising scientific text in a way that is tailored to specific types of users, and in their native language. For this objective, we used an LLM that our system queries using user-selected parameters. We conducted an informal evaluation of this prototype using a questionnaire in 3 different languages.
Anthology ID:
2024.determit-1.18
Volume:
Proceedings of the Workshop on DeTermIt! Evaluating Text Difficulty in a Multilingual Context @ LREC-COLING 2024
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Giorgio Maria Di Nunzio, Federica Vezzani, Liana Ermakova, Hosein Azarbonyad, Jaap Kamps
Venues:
DeTermIt | WS
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
196–201
Language:
URL:
https://aclanthology.org/2024.determit-1.18
DOI:
Bibkey:
Cite (ACL):
Nico Colic, Jin-Dong Kim, and Fabio Rinaldi. 2024. Pre-Gamus: Reducing Complexity of Scientific Literature as a Support against Misinformation. In Proceedings of the Workshop on DeTermIt! Evaluating Text Difficulty in a Multilingual Context @ LREC-COLING 2024, pages 196–201, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Pre-Gamus: Reducing Complexity of Scientific Literature as a Support against Misinformation (Colic et al., DeTermIt-WS 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.determit-1.18.pdf