# Unsupervised Multi-Granularity Summarization

arXiv: https://arxiv.org/abs/2201.12502 · HTML: https://mlsanity.com/paper/2201.12502/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 57 references that are in the map.
- First posted on arXiv 29 Jan 2022.

## Builds on (most cited first, 30 of 31)

- [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)
- [BARTScore: Evaluating Generated Text as Text Generation](https://mlsanity.com/paper/2106.11520/) (2021)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents](https://mlsanity.com/paper/1804.05685/) (2018)
- [Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model](https://mlsanity.com/paper/1906.01749/) (2019)
- [QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization](https://mlsanity.com/paper/2104.05938/) (2021)
- [Extractive Summarization as Text Matching](https://mlsanity.com/paper/2004.08795/) (2020)
- [DialogSum: A Real-Life Scenario Dialogue Summarization Dataset](https://mlsanity.com/paper/2105.06762/) (2021)
- [GSum: A General Framework for Guided Neural Abstractive Summarization](https://mlsanity.com/paper/2010.08014/) (2020)
- [Controllable Abstractive Summarization](https://mlsanity.com/paper/1711.05217/) (2017)
- [CTRLsum: Towards Generic Controllable Text Summarization](https://mlsanity.com/paper/2012.04281/) (2020)
- [Heterogeneous Graph Neural Networks for Extractive Document Summarization](https://mlsanity.com/paper/2004.12393/) (2020)
- [ASER: A Large-scale Eventuality Knowledge Graph](https://mlsanity.com/paper/1905.00270/) (2019)
- [Controlling Output Length in Neural Encoder-Decoders](https://mlsanity.com/paper/1609.09552/) (2016)
- [Sentence Centrality Revisited for Unsupervised Summarization](https://mlsanity.com/paper/1906.03508/) (2019)
- [PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization](https://mlsanity.com/paper/2110.08499/) (2021)
- [WikiAsp: A Dataset for Multi-domain Aspect-based Summarization](https://mlsanity.com/paper/2011.07832/) (2020)
- [Searching for Effective Neural Extractive Summarization: What Works and What's Next](https://mlsanity.com/paper/1907.03491/) (2019)
- [TED: A Pretrained Unsupervised Summarization Model with Theme Modeling and Denoising](https://mlsanity.com/paper/2001.00725/) (2020)
- [Unsupervised Extractive Summarization using Pointwise Mutual Information](https://mlsanity.com/paper/2102.06272/) (2021)
- [RetrievalSum: A Retrieval Enhanced Framework for Abstractive Summarization](https://mlsanity.com/paper/2109.07943/) (2021)
- [A Closer Look at Data Bias in Neural Extractive Summarization Models](https://mlsanity.com/paper/1909.13705/) (2019)
- [CDEvalSumm: An Empirical Study of Cross-Dataset Evaluation for Neural Summarization Systems](https://mlsanity.com/paper/2010.05139/) (2020)
- [Unsupervised Extractive Summarization by Pre-training Hierarchical Transformers](https://mlsanity.com/paper/2010.08242/) (2020)
- [Exploring Domain Shift in Extractive Text Summarization](https://mlsanity.com/paper/1908.11664/) (2019)
- [Learning to Encode Text as Human-Readable Summaries using Generative Adversarial Networks](https://mlsanity.com/paper/1810.02851/) (2018)

## Built on by (most cited first, 5 of 5)

- [A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Models](https://mlsanity.com/paper/2406.11289/) (2024)
- [Summarization is (Almost) Dead](https://mlsanity.com/paper/2309.09558/) (2023)
- [EventGround: Narrative Reasoning by Grounding to Eventuality-centric Knowledge Graphs](https://mlsanity.com/paper/2404.00209/) (2024)
- [Rewriting the Script: Adapting Text Instructions for Voice Interaction](https://mlsanity.com/paper/2306.09992/) (2023)
- [Query-Guided Self-Supervised Summarization of Nursing Notes](https://mlsanity.com/paper/2407.04125/) (2024)

## Abstract (arXiv)

Text summarization is a user-preference based task, i.e., for one document, users often have different priorities for summary. As a key aspect of customization in summarization, granularity is used to measure the semantic coverage between the summary and source document. However, developing systems that can generate summaries with customizable semantic coverage is still an under-explored topic. In this paper, we propose the first unsupervised multi-granularity summarization framework, GranuSum. We take events as the basic semantic units of the source documents and propose to rank these events by their salience. We also develop a model to summarize input documents with given events as anchors and hints. By inputting different numbers of events, GranuSum is capable of producing multi-granular summaries in an unsupervised manner. Meanwhile, we annotate a new benchmark GranuDUC that contains multiple summaries at different granularities for each document cluster. Experimental results confirm the substantial superiority of GranuSum on multi-granularity summarization over strong baselines. Further, by exploiting the event information, GranuSum also exhibits state-of-the-art performance under the conventional unsupervised abstractive setting. Dataset for this paper can be found at: https://github.com/maszhongming/GranuDUC