# Science Checker: Extractive-Boolean Question Answering For Scientific Fact Checking

Source: [Science Checker: Extractive-Boolean Question Answering For Scientific Fact Checking](<https://www.sota2.com/research/paper/68fa6c621e209131cea33bb0>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:09:48.320Z

Authors: Lo\\"ic Rakotoson, Charles Letaillieur, Sylvain Massip, Fr\\'ejus Laleye

Published: 2022-04-26T12:35:23.000Z

## Abstract

With the explosive growth of scientific publications, making the synthesis of scientific knowledge and fact checking becomes an increasingly complex task. In this paper, we propose a multi-task approach for verifying the scientific questions based on a joint reasoning from facts and evidence in research articles. We propose an intelligent combination of (1) an automatic information summarization and (2) a Boolean Question Answering which allows to generate an answer to a scientific question from only extracts obtained after summarization. Thus on a given topic, our proposed approach conducts structured content modeling based on paper abstracts to answer a scientific question while highlighting texts from paper that discuss the topic. We based our final system on an end-to-end Extractive Question Answering (EQA) combined with a three outputs classification model to perform in-depth semantic understanding of a question to illustrate the aggregation of multiple responses. With our light and fast proposed architecture, we achieved an average error rate of 4% and a F1-score of 95.6%. Our results are supported via experiments with two QA models (BERT, RoBERTa) over 3 Million Open Access (OA) articles in the medical and health domains on Europe PMC.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2204.12263>)

[PDF](<https://arxiv.org/pdf/2204.12263.pdf>)