KAAS: A Keyword-Aware Attention Abstractive Summarization Model for Scientific Articles

ABSTRACT

In this work, we focus on ive summarization methods for assisting medical researchers in effectively managing information. Particularly, we introduce a COVID-19-related summarization dataset (COVID-SUM) and propose a novel Keyword-aware Attention ive Summarization (KAAS) model. The KAAS model consists of two encoders and one decoder. As for the encoders, one is a standard article encoder built on transformer layers, while the other one is a hierarchical keyword encoder that first encodes the words in a keyword using BiLSTM, and then passes the keyword representations to a transformer layer to connect the keywords in an example. Additionally, a decoder with keyword-focused attention is utilized to further direct the decoding process to generate comprehensive summaries of the scientific articles. We benchmark several summarization methods on the new COVID-SUM dataset and release this dataset in the hope to promote advances to summarization in the COVID-19 medical area (https//github.com/ccip-author/COVID-SUM/releases ). Furthermore, we evaluate the KAAS on COVID-SUM, ArXiv, and PubMed datasets. Experimental results demonstrate that KAAS outperforms several state-of-the-art models on these datasets. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.