HTSS: A Novel Hybrid Text Summarisation and Simplification Architecture

Text simplification and text summarisation are related, but different sub-tasks in Natural Language Generation. Whereas summarisation attempts to reduce the length ofa document, whilst keeping the original meaning, simplification attempts to reducethe complexity of a document. In this work, we combine both tasks of summarisation and simplification using a novel hybrid architecture of abstractive and extractivesummarisation called HTSS. We extend the well-known pointer generator model forthe combined task of summarisation and simplification. We have collected our parallel corpus from the simplified summaries written by domain experts published on thescience news website EurekaAlert (www.eurekalert.org). Our results show thatour proposed HTSS model outperforms neural text simplification (NTS) on SARI scoreand abstractive text summarisation (ATS) on the ROUGE score. We further introducea 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 andsummarisation by 38.94% and 53.40%, respectively.We provide all code, models and corpora to the scientific community for futureresearch at the following URL: https://github.com/slab-itu/HTSS/.