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/.

文本简化和文本摘要是相关的，但是自然语言生成中的子任务不同。总结试图在保持原始含义的同时减少文档的长度，而简化则试图降低文档的复杂性。在这项工作中，我们使用称为HTSS的抽象和提取摘要的新型混合体系结构，将摘要和简化任务结合在一起。我们将众所周知的指针生成器模型扩展为汇总和简化的组合任务。我们从科学新闻网站EurekaAlert（www.eurekalert.org）上发布的领域专家撰写的简化摘要中收集了平行语料库。我们的结果表明，我们提出的HTSS模型在SARI评分上优于神经文本简化（NTS），在ROUGE评分上优于抽象文本摘要（ATS）。我们进一步介绍了一个新指标（CSS₁）结合了SARI和Rouge，并证明了我们提出的HTSS模型在简化和汇总的共同任务上分别比NTS和ATS优越38.94％和53.40％。我们通过以下URL向科学界提供所有代码，模型和语料库，以供将来研究：https://github.com/slab-itu/HTSS/。