Papers › SATS: simplification aware text summarization of scientific documents
SATS: simplification aware text summarization of scientific documents
Farooq Zaman, Faisal Kamiran, Matthew Shardlow, Saeed-Ul Hassan, Asim Karim, Naif Radi Aljohani
Simplifying summaries of scholarly publications has been a popular method for conveying scientific discoveries to a broader audience. While text summarization 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 study presents a new Simplification Aware Text Summarization model (SATS) based on future n-gram prediction. The proposed SATS model extends ProphetNet, a text summarization 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 summarization and simplification corpus consisting of 5,400 scientific article pairs. Our results in terms of automatic evaluation demonstrate that SATS outperforms state-of-the-art models for simplification, summarization, and joint simplification-summarization across two datasets on ROUGE, SARI, and CSS1. We also provide human evaluation of summaries generated by the SATS model. We evaluated 100 summaries from eight annotators for grammar, coherence, consistency, fluency, and simplicity. The average human judgment for all evaluated dimensions lies between 4.0 and 4.5 on a scale from 1 to 5 where 1 means low and 5 means high.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Text Simplification | EurekaAlert | SATS | SARI | 40.83 | #3 of 3 | Archive leaderboard | report |
| Text Summarization | EurekaAlert | SATS | Avg. Test Rouge1 | 34.24 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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