Enhancing Abstractive Summarization of Scientific Papers Using Structure Information

The code and dataset can be accessed at https://github.com/tongbao96/code - for - SFR - AS 1. Introduction W ith the rapid growth of scientific research and the academic community, numerous scientific papers are published daily. This notable increase in publications has led to information overload and requiring schol a r s to spend considerable time in reading and comprehending a large volume of articles . The goal of automatic summarization is to employ algorithms to extract key information and reorganize it into shorter, concise summaries (El - Kassas et al., 2021) . Automatic summarization holds significant research value in fields such as information retrieval (Spina et al., 2017), question and answer system (Y ulianti et al., 2018), and content review (Hu et al., 2017) . Existing a utomatic summarization methods are broadly divided into two categories: extractive method s and abstractive methods . Extractive methods generate summaries by selecting sentences directly from the original document, resulting in summaries that are more accurate and semantically consistent but may lack coherence. In contrast, abstractive methods generate summaries based on an understanding of the text, rather than extracting sentences directly from the original document . Therefore, summaries produced by this approach are typically more coherent and better aligned with human reading preferences (El - Kassas et al., 2021; Ghadimi & Beigy, 2022) . In this paper, we focus on abstractive summarization .