Zaman Farooq, Kamiran Faisal, Shardlow Matthew, Hassan Saeed-Ul, Karim Asim, Aljohani Naif Radi
Scientometrics Lab, Information Technology University, Lahore, Pakistan.
Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, United Kingdom.
Front Artif Intell. 2024 Jul 10;7:1375419. doi: 10.3389/frai.2024.1375419. eCollection 2024.
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 . 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.
简化学术出版物的摘要一直是向更广泛受众传达科学发现的常用方法。虽然文本摘要旨在缩短长篇文档，但简化则力求降低文档的复杂性。为了共同完成这些任务，需要开发机器学习方法来缩短和简化较长的文本。本研究提出了一种基于未来n元语法预测的新型简化感知文本摘要模型（SATS）。所提出的SATS模型通过使用词频词典增强目标函数以用于简化任务，对文本摘要模型ProphetNet进行了扩展。我们在一个最近发布的由5400对科学文章组成的文本摘要和简化语料库上评估了SATS的性能。我们在自动评估方面的结果表明，在两个数据集上，就ROUGE、SARI和 而言，SATS在简化、摘要以及联合简化-摘要方面均优于现有最先进的模型。我们还对SATS模型生成的摘要进行了人工评估。我们让八位注释者对100篇摘要的语法、连贯性、一致性、流畅性和简洁性进行了评估。在从1到5的评分量表上（1表示低，5表示高），所有评估维度的平均人工判断介于4.0和4.5之间。
