# Salience Allocation as Guidance for Abstractive Summarization arXiv: https://arxiv.org/abs/2210.12330 ยท HTML: https://mlsanity.com/paper/2210.12330/ ## Key facts - Cited by 14 papers in the map (data through 14 Oct 2026). - Builds on 37 of its 53 references that are in the map. - First posted on arXiv 22 Oct 2022. ## Builds on (most cited first, 30 of 37) - [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019) - [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020) - [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019) - [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) - [When Does Label Smoothing Help?](https://mlsanity.com/paper/1906.02629/) (2019) - [On Faithfulness and Factuality in Abstractive Summarization](https://mlsanity.com/paper/2005.00661/) (2020) - [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016) - [BARTScore: Evaluating Generated Text as Text Generation](https://mlsanity.com/paper/2106.11520/) (2021) - [SummEval: Re-evaluating Summarization Evaluation](https://mlsanity.com/paper/2007.12626/) (2020) - [A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents](https://mlsanity.com/paper/1804.05685/) (2018) - [A Neural Attention Model for Abstractive Sentence Summarization](https://mlsanity.com/paper/1509.00685/) (2015) - [Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies](https://mlsanity.com/paper/1804.11283/) (2018) - [WikiHow: A Large Scale Text Summarization Dataset](https://mlsanity.com/paper/1810.09305/) (2018) - [Extractive Summarization as Text Matching](https://mlsanity.com/paper/2004.08795/) (2020) - [Bottom-Up Abstractive Summarization](https://mlsanity.com/paper/1808.10792/) (2018) - [BRIO: Bringing Order to Abstractive Summarization](https://mlsanity.com/paper/2203.16804/) (2022) - [Adaptive Attention Span in Transformers](https://mlsanity.com/paper/1905.07799/) (2019) - [SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization](https://mlsanity.com/paper/2106.01890/) (2021) - [GSum: A General Framework for Guided Neural Abstractive Summarization](https://mlsanity.com/paper/2010.08014/) (2020) - [Fine-tune BERT for Extractive Summarization](https://mlsanity.com/paper/1903.10318/) (2019) - [Learning to Deceive with Attention-Based Explanations](https://mlsanity.com/paper/1909.07913/) (2019) - [On Extractive and Abstractive Neural Document Summarization with Transformer Language Models](https://mlsanity.com/paper/1909.03186/) (2019) - [DocNLI: A Large-scale Dataset for Document-level Natural Language Inference](https://mlsanity.com/paper/2106.09449/) (2021) - [A Unified Model for Extractive and Abstractive Summarization using Inconsistency Loss](https://mlsanity.com/paper/1805.06266/) (2018) - [Searching for Effective Neural Extractive Summarization: What Works and What's Next](https://mlsanity.com/paper/1907.03491/) (2019) - [Robust (Controlled) Table-to-Text Generation with Structure-Aware Equivariance Learning](https://mlsanity.com/paper/2205.03972/) (2022) - [Summarization as Indirect Supervision for Relation Extraction](https://mlsanity.com/paper/2205.09837/) (2022) - [Long-Span Summarization via Local Attention and Content Selection](https://mlsanity.com/paper/2105.03801/) (2021) - [Controlling the Amount of Verbatim Copying in Abstractive Summarization](https://mlsanity.com/paper/1911.10390/) (2019) ## Built on by (most cited first, 14 of 14) - [Extractive Summarization via ChatGPT for Faithful Summary Generation](https://mlsanity.com/paper/2304.04193/) (2023) - [A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Models](https://mlsanity.com/paper/2406.11289/) (2024) - [OASum: Large-Scale Open Domain Aspect-based Summarization](https://mlsanity.com/paper/2212.09233/) (2022) - [Robust Natural Language Understanding with Residual Attention Debiasing](https://mlsanity.com/paper/2305.17627/) (2023) - [Select and Summarize: Scene Saliency for Movie Script Summarization](https://mlsanity.com/paper/2404.03561/) (2024) - [Leveraging Summary Guidance on Medical Report Summarization](https://mlsanity.com/paper/2302.04001/) (2023) - [From Instructions to Constraints: Language Model Alignment with Automatic Constraint Verification](https://mlsanity.com/paper/2403.06326/) (2024) - [TracSum: A New Benchmark for Aspect-Based Summarization with Sentence-Level Traceability in Medical Domain](https://mlsanity.com/paper/2508.13798/) (2025) - [Open-Domain Event Graph Induction for Mitigating Framing Bias](https://mlsanity.com/paper/2305.12835/) (2023) - [Surveying the Landscape of Text Summarization with Deep Learning: A Comprehensive Review](https://mlsanity.com/paper/2310.09411/) (2023) - [Analysis of Multidomain Abstractive Summarization Using Salience Allocation](https://mlsanity.com/paper/2402.11955/) (2024) - [NumHG: A Dataset for Number-Focused Headline Generation](https://mlsanity.com/paper/2309.01455/) (2023) - [Prompt-based Pseudo-labeling Strategy for Sample-Efficient Semi-Supervised Extractive Summarization](https://mlsanity.com/paper/2311.09559/) (2023) - [Extract-and-Abstract: Unifying Extractive and Abstractive Summarization within Single Encoder-Decoder Framework](https://mlsanity.com/paper/2409.11827/) (2024) ## Abstract (arXiv) Abstractive summarization models typically learn to capture the salient information from scratch implicitly. Recent literature adds extractive summaries as guidance for abstractive summarization models to provide hints of salient content and achieves better performance. However, extractive summaries as guidance could be over strict, leading to information loss or noisy signals. Furthermore, it cannot easily adapt to documents with various abstractiveness. As the number and allocation of salience content pieces vary, it is hard to find a fixed threshold deciding which content should be included in the guidance. In this paper, we propose a novel summarization approach with a flexible and reliable salience guidance, namely SEASON (SaliencE Allocation as Guidance for Abstractive SummarizatiON). SEASON utilizes the allocation of salience expectation to guide abstractive summarization and adapts well to articles in different abstractiveness. Automatic and human evaluations on two benchmark datasets show that the proposed method is effective and reliable. Empirical results on more than one million news articles demonstrate a natural fifteen-fifty salience split for news article sentences, providing a useful insight for composing news articles.