Analysis of Multidomain Abstractive Summarization Using Salience
  Allocation
Tohida Rehman Raghubir Bose Soumik Dey Samiran Chattopadhyay
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.