Automatic text summarization has been widely studied as an important task in
natural language processing. Traditionally, various feature engineering and
machine learning based systems have been proposed for extractive as well as
abstractive text summarization. Recently, deep learning based, specifically
Transformer-based systems have been immensely popular. Summarization is a
cognitively challenging task - extracting summary worthy sentences is
laborious, and expressing semantics in brief when doing abstractive
summarization is complicated. In this paper, we specifically look at the
problem of summarizing scientific research papers from multiple domains. We
differentiate between two types of summaries, namely, (a) LaySumm: A very short
summary that captures the essence of the research paper in layman terms
restricting overtly specific technical jargon and (b) LongSumm: A much longer
detailed summary aimed at providing specific insights into various ideas
touched upon in the paper. While leveraging latest Transformer-based models,
our systems are simple, intuitive and based on how specific paper sections
contribute to human summaries of the two types described above. Evaluations
against gold standard summaries using ROUGE metrics prove the effectiveness of
our approach. On blind test corpora, our system ranks first and third for the
LongSumm and LaySumm tasks respectively.

本文探讨了利用 Transformer-based 系统概括多领域科学研究论文的问题，并设计了两种不同类型的概述手段，即 LaySumm 和 LongSumm，使用 ROUGE 指标有效评估了本文系统的优越性。