# An Efficient Coarse-to-Fine Facet-Aware Unsupervised Summarization Framework based on Semantic Blocks

arXiv: https://arxiv.org/abs/2208.08253 · HTML: https://mlsanity.com/paper/2208.08253/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 48 references that are in the map.
- First posted on arXiv 17 Aug 2022.

## Builds on (most cited first, 27 of 27)

- [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)
- [Big Bird: Transformers for Longer Sequences](https://mlsanity.com/paper/2007.14062/) (2020)
- [Reformer: The Efficient Transformer](https://mlsanity.com/paper/2001.04451/) (2020)
- [Generating Long Sequences with Sparse Transformers](https://mlsanity.com/paper/1904.10509/) (2019)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [Long Range Arena: A Benchmark for Efficient Transformers](https://mlsanity.com/paper/2011.04006/) (2020)
- [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)
- [Efficient Attentions for Long Document Summarization](https://mlsanity.com/paper/2104.02112/) (2021)
- [Extractive Summarization as Text Matching](https://mlsanity.com/paper/2004.08795/) (2020)
- [BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization](https://mlsanity.com/paper/1906.03741/) (2019)
- [SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization](https://mlsanity.com/paper/2106.01890/) (2021)
- [HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization](https://mlsanity.com/paper/1905.06566/) (2019)
- [BillSum: A Corpus for Automatic Summarization of US Legislation](https://mlsanity.com/paper/1910.00523/) (2019)
- [Leveraging Graph to Improve Abstractive Multi-Document Summarization](https://mlsanity.com/paper/2005.10043/) (2020)
- [Sentence Centrality Revisited for Unsupervised Summarization](https://mlsanity.com/paper/1906.03508/) (2019)
- [Neural Extractive Text Summarization with Syntactic Compression](https://mlsanity.com/paper/1902.00863/) (2019)
- [Scoring Sentence Singletons and Pairs for Abstractive Summarization](https://mlsanity.com/paper/1906.00077/) (2019)
- [TED: A Pretrained Unsupervised Summarization Model with Theme Modeling and Denoising](https://mlsanity.com/paper/2001.00725/) (2020)
- [RefSum: Refactoring Neural Summarization](https://mlsanity.com/paper/2104.07210/) (2021)
- [Simple Unsupervised Summarization by Contextual Matching](https://mlsanity.com/paper/1907.13337/) (2019)
- [Unsupervised Extractive Summarization by Pre-training Hierarchical Transformers](https://mlsanity.com/paper/2010.08242/) (2020)
- [Facet-Aware Evaluation for Extractive Summarization](https://mlsanity.com/paper/1908.10383/) (2019)

## Built on by (most cited first, 4 of 4)

- [Hybrid Long Document Summarization using C2F-FAR and ChatGPT: A Practical Study](https://mlsanity.com/paper/2306.01169/) (2023)
- [Modeling Paragraph-Level Vision-Language Semantic Alignment for Multi-Modal Summarization](https://mlsanity.com/paper/2208.11303/) (2022)
- [Recent Trends in Unsupervised Summarization](https://mlsanity.com/paper/2305.11231/) (2023)
- [Controlled Randomness Improves the Performance of Transformer Models](https://mlsanity.com/paper/2310.13526/) (2023)

## Abstract (arXiv)

Unsupervised summarization methods have achieved remarkable results by incorporating representations from pre-trained language models. However, existing methods fail to consider efficiency and effectiveness at the same time when the input document is extremely long. To tackle this problem, in this paper, we proposed an efficient Coarse-to-Fine Facet-Aware Ranking (C2F-FAR) framework for unsupervised long document summarization, which is based on the semantic block. The semantic block refers to continuous sentences in the document that describe the same facet. Specifically, we address this problem by converting the one-step ranking method into the hierarchical multi-granularity two-stage ranking. In the coarse-level stage, we propose a new segment algorithm to split the document into facet-aware semantic blocks and then filter insignificant blocks. In the fine-level stage, we select salient sentences in each block and then extract the final summary from selected sentences. We evaluate our framework on four long document summarization datasets: Gov-Report, BillSum, arXiv, and PubMed. Our C2F-FAR can achieve new state-of-the-art unsupervised summarization results on Gov-Report and BillSum. In addition, our method speeds up 4-28 times more than previous methods.footnoteurlhttps://github.com/xnliang98/c2f-far