# Analysis of Multidomain Abstractive Summarization Using Salience Allocation

arXiv: https://arxiv.org/abs/2402.11955 · HTML: https://mlsanity.com/paper/2402.11955/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 35 references that are in the map.
- First posted on arXiv 19 Feb 2024.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016)
- [SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization](https://mlsanity.com/paper/1911.12237/) (2019)
- [MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance](https://mlsanity.com/paper/1909.02622/) (2019)
- [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://mlsanity.com/paper/2001.04063/) (2020)
- [Bottom-Up Abstractive Summarization](https://mlsanity.com/paper/1808.10792/) (2018)
- [Entity-level Factual Consistency of Abstractive Text Summarization](https://mlsanity.com/paper/2102.09130/) (2021)
- [On Extractive and Abstractive Neural Document Summarization with Transformer Language Models](https://mlsanity.com/paper/1909.03186/) (2019)
- [A Unified Model for Extractive and Abstractive Summarization using Inconsistency Loss](https://mlsanity.com/paper/1805.06266/) (2018)
- [Trade the Event: Corporate Events Detection for News-Based Event-Driven Trading](https://mlsanity.com/paper/2105.12825/) (2021)
- [Robust (Controlled) Table-to-Text Generation with Structure-Aware Equivariance Learning](https://mlsanity.com/paper/2205.03972/) (2022)
- [Long-Span Summarization via Local Attention and Content Selection](https://mlsanity.com/paper/2105.03801/) (2021)
- [Abstractive Summarization with Combination of Pre-trained Sequence-to-Sequence and Saliency Models](https://mlsanity.com/paper/2003.13028/) (2020)
- [Salience Allocation as Guidance for Abstractive Summarization](https://mlsanity.com/paper/2210.12330/) (2022)
- [Generation of Highlights from Research Papers Using Pointer-Generator Networks and SciBERT Embeddings](https://mlsanity.com/paper/2302.07729/) (2023)
- [Enhancing Factual Consistency of Abstractive Summarization](https://mlsanity.com/paper/2003.08612/) (2020)
- [Named Entity Recognition Based Automatic Generation of Research Highlights](https://mlsanity.com/paper/2303.12795/) (2023)
- [Abstractive Text Summarization based on Language Model Conditioning and Locality Modeling](https://mlsanity.com/paper/2003.13027/) (2020)
- [An Analysis of Abstractive Text Summarization Using Pre-trained Models](https://mlsanity.com/paper/2303.12796/) (2023)
- [Hallucination Reduction in Long Input Text Summarization](https://mlsanity.com/paper/2309.16781/) (2023)

## Built on by (most cited first, 2 of 2)

- [Evaluating LLMs and Pre-trained Models for Text Summarization Across Diverse Datasets](https://mlsanity.com/paper/2502.19339/) (2025)
- [Comparative Analysis of Abstractive Summarization Models for Clinical Radiology Reports](https://mlsanity.com/paper/2506.16247/) (2025)

## Abstract (arXiv)

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.