This paper explores the realm of abstractive text summarization through the lens of the SEASON (Salience Allocation as Guidance for Abstractive SummarizatiON) technique, a model designed to enhance summarization by leveraging salience allocation techniques. The study evaluates SEASON's efficacy by comparing it with prominent models like BART, PEGASUS, and ProphetNet, all fine-tuned for various text summarization tasks. The assessment is conducted using diverse datasets including CNN/Dailymail, SAMSum, and Financial-news based Event-Driven Trading (EDT), with a specific focus on a financial dataset containing a substantial volume of news articles from 2020/03/01 to 2021/05/06. This paper employs various evaluation metrics such as ROUGE, METEOR, BERTScore, and MoverScore to evaluate the performance of these models fine-tuned for generating abstractive summaries. The analysis of these metrics offers a thorough insight into the strengths and weaknesses demonstrated by each model in summarizing news dataset, dialogue dataset and financial text dataset. The results presented in this paper not only contribute to the evaluation of the SEASON model's effectiveness but also illuminate the intricacies of salience allocation techniques across various types of datasets.

翻译：本文通过SEASON（显著度分配引导抽象式摘要生成）技术的视角探索了抽象式文本摘要领域，该模型旨在通过利用显著度分配技术来增强摘要生成效果。研究将SEASON与BART、PEGASUS及ProphetNet等主流模型进行对比评估，这些模型均针对多种文本摘要任务进行了微调。评估采用多样化数据集，包括CNN/Dailymail、SAMSum及基于金融新闻的事件驱动交易（EDT）数据集，特别关注一个包含2020年3月1日至2021年5月6日期间大量新闻文章的金融数据集。本文采用ROUGE、METEOR、BERTScore和MoverScore等多种评估指标，对经微调后用于生成抽象式摘要的模型性能进行评价。这些指标的分析深入揭示了各模型在新闻数据集、对话数据集及金融文本数据集摘要任务中展现的优缺点。本文结果不仅有助于评估SEASON模型的有效性，还阐明了跨不同类型数据集的显著度分配技术的复杂特性。