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.

本文提出了一种基于多任务学习的科学问题验证方法，该方法结合了信息概括、布尔问答、提取式问题回答和语义理解技术。该方法在欧洲 PMC 的 300 万篇医学和健康领域 OA 文章上，在 BERT 和 RoBERTa 问答模型的实验中，实现了 4％的平均误差率和 95.6％的 F1 分数。