SATS: simplification aware text summarization of scientific documents - Zaman, Farooq; Kamiran, Faisal; Shardlow, Matthew; Hassan, Saeed-Ul; Karim, Asim; Aljohani, Naif Radi (Frontiers in Artificial Intelligence, 2024)
Simplifying summaries of scholarly publications has been a popular method for conveying scientific discoveries to a broader audience. While text summarisation aims to shorten long documents, simplification seeks to reduce the complexity of a document. To accomplish these tasks collectively, there is a need to develop machine learning methods to shorten and simplify longer texts. This paper presents a new Simplification Aware Text Summarisation model (SATS) based on future n-gram prediction. The proposed SATS model extends ProphetNet, a text summarisation model, by enhancing the objective function using a word frequency lexicon for simplification tasks. We have evaluated the performance of SATS on a recently published text summarisation and simplification corpus consisting of 5400 scientific article pairs. Our results in terms of automatic evaluation demonstrate that SATS outperforms state-of-the-art models for simplification, summarisation and joint simplification-summarisation across two datasets on ROUGE, SARI and CSS 1 . We also provide human evaluation of summaries generated by the SATS model. We evaluated 100 summaries from 8 annotators for grammar, coherence,