In the past decade, many scientific news media that report scientific
breakthroughs and discoveries emerged, bringing science and technology closer
to the general public. However, not all scientific news article cites proper
sources, such as original scientific papers. A portion of scientific news
articles contain misinterpreted, exaggerated, or distorted information that
deviates from facts asserted in the original papers. Manually identifying
proper citations is laborious and costly. Therefore, it is necessary to
automatically search for pertinent scientific papers that could be used as
evidence for a given piece of scientific news. We propose a system called SciEv
that searches for scientific evidence papers given a scientific news article.
The system employs a 2-stage query paradigm with the first stage retrieving
candidate papers and the second stage reranking them. The key feature of SciEv
is it uses domain knowledge entities (DKEs) to find candidates in the first
stage, which proved to be more effective than regular keyphrases. In the
reranking stage, we explore different document representations for news
articles and candidate papers. To evaluate our system, we compiled a pilot
dataset consisting of 100 manually curated (news,paper) pairs from ScienceAlert
and similar websites. To our best knowledge, this is the first dataset of this
kind. Our experiments indicate that the transformer model performs the best for
DKE extraction. The system achieves a P@1=50%, P@5=71%, and P@10=74% when it
uses a TFIDF-based text representation. The transformer-based re-ranker
achieves a comparable performance but costs twice as much time. We will collect
more data and test the system for user experience.

SciEv 是一个系统，可以在给定科学新闻文章的情况下搜索科学证据论文，并采用两阶段查询范式以检索候选论文并进行重新排序，它使用领域知识实体来找到候选论文，证明比常规关键短语更有效，并在重排阶段中探索了新闻文章和候选论文的不同文档表示方法。