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HTSS: A novel hybrid text summarisation and simplification architecture

•This paper presents a new hybrid loss function that enables the text summarisation model to generate easy-to-read simplified summaries.•A new evaluation measure for the combined tasks of summarisation and simplification has been proposed.•A novel parallel corpus of 5204 articles with their associat...

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Bibliographic Details
Published in:Information processing & management 2020-11, Vol.57 (6), p.102351, Article 102351
Main Authors: Zaman, Farooq, Shardlow, Matthew, Hassan, Saeed-Ul, Aljohani, Naif Radi, Nawaz, Raheel
Format: Article
Language:English
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Summary:•This paper presents a new hybrid loss function that enables the text summarisation model to generate easy-to-read simplified summaries.•A new evaluation measure for the combined tasks of summarisation and simplification has been proposed.•A novel parallel corpus of 5204 articles with their associated summarised simplified text for the combined task of text summasization and simplification has been provided for future research.•The proposed hybrid approach outperforms existing state-of-the-art neural text simplification and abstractive text summarisation models by 38.94% and 53.40%, respectively. Text simplification and text summarisation are related, but different sub-tasks in Natural Language Generation. Whereas summarisation attempts to reduce the length of a document, whilst keeping the original meaning, simplification attempts to reduce the complexity of a document. In this work, we combine both tasks of summarisation and simplification using a novel hybrid architecture of abstractive and extractive summarisation called HTSS. We extend the well-known pointer generator model for the combined task of summarisation and simplification. We have collected our parallel corpus from the simplified summaries written by domain experts published on the science news website EurekaAlert (www.eurekalert.org). Our results show that our proposed HTSS model outperforms neural text simplification (NTS) on SARI score and abstractive text summarisation (ATS) on the ROUGE score. We further introduce a new metric (CSS1) which combines SARI and Rouge and demonstrates that our proposed HTSS model outperforms NTS and ATS on the joint task of simplification and summarisation by 38.94% and 53.40%, respectively. We provide all code, models and corpora to the scientific community for future research at the following URL: https://github.com/slab-itu/HTSS/.
ISSN:0306-4573
1873-5371
DOI:10.1016/j.ipm.2020.102351