# Scoring Sentence Singletons and Pairs for Abstractive Summarization

arXiv: https://arxiv.org/abs/1906.00077 · HTML: https://mlsanity.com/paper/1906.00077/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2019.

## Built on by (most cited first, 30 of 34)

- [QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization](https://mlsanity.com/paper/2104.05938/) (2021)
- [A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Models](https://mlsanity.com/paper/2406.11289/) (2024)
- [What's in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarization](https://mlsanity.com/paper/2105.00816/) (2021)
- [DYLE: Dynamic Latent Extraction for Abstractive Long-Input Summarization](https://mlsanity.com/paper/2110.08168/) (2021)
- [Learning to Revise References for Faithful Summarization](https://mlsanity.com/paper/2204.10290/) (2022)
- [Fine-Grained Natural Language Inference Based Faithfulness Evaluation for Diverse Summarisation Tasks](https://mlsanity.com/paper/2402.17630/) (2024)
- [Controllable Summarization with Constrained Markov Decision Process](https://mlsanity.com/paper/2108.03405/) (2021)
- [QA-Align: Representing Cross-Text Content Overlap by Aligning Question-Answer Propositions](https://mlsanity.com/paper/2109.12655/) (2021)
- [Proposition-Level Clustering for Multi-Document Summarization](https://mlsanity.com/paper/2112.08770/) (2021)
- [Summarization Programs: Interpretable Abstractive Summarization with Neural Modular Trees](https://mlsanity.com/paper/2209.10492/) (2022)
- [A Meta-Evaluation of Faithfulness Metrics for Long-Form Hospital-Course Summarization](https://mlsanity.com/paper/2303.03948/) (2023)
- [A New Approach to Overgenerating and Scoring Abstractive Summaries](https://mlsanity.com/paper/2104.01726/) (2021)
- [Enriching Transformers with Structured Tensor-Product Representations for Abstractive Summarization](https://mlsanity.com/paper/2106.01317/) (2021)
- [Leveraging Locality in Abstractive Text Summarization](https://mlsanity.com/paper/2205.12476/) (2022)
- [Generating EDU Extracts for Plan-Guided Summary Re-Ranking](https://mlsanity.com/paper/2305.17779/) (2023)
- [Towards Abstractive Grounded Summarization of Podcast Transcripts](https://mlsanity.com/paper/2203.11425/) (2022)
- [OTExtSum: Extractive Text Summarisation with Optimal Transport](https://mlsanity.com/paper/2204.10086/) (2022)
- [The Power of Summary-Source Alignments](https://mlsanity.com/paper/2406.00842/) (2024)
- [SummerTime: Text Summarization Toolkit for Non-experts](https://mlsanity.com/paper/2108.12738/) (2021)
- [Extending Multi-Text Sentence Fusion Resources via Pyramid Annotations](https://mlsanity.com/paper/2110.04517/) (2021)
- [USB: A Unified Summarization Benchmark Across Tasks and Domains](https://mlsanity.com/paper/2305.14296/) (2023)
- [Summary Explorer: Visualizing the State of the Art in Text Summarization](https://mlsanity.com/paper/2108.01879/) (2021)
- [An Efficient Coarse-to-Fine Facet-Aware Unsupervised Summarization Framework based on Semantic Blocks](https://mlsanity.com/paper/2208.08253/) (2022)
- [Improving Radiology Summarization with Radiograph and Anatomy Prompts](https://mlsanity.com/paper/2210.08303/) (2022)
- [Abstractive Summarization Guided by Latent Hierarchical Document Structure](https://mlsanity.com/paper/2211.09458/) (2022)
- [GenerationPrograms: Fine-grained Attribution with Executable Programs](https://mlsanity.com/paper/2506.14580/) (2025)
- [Summarizing Community-based Question-Answer Pairs](https://mlsanity.com/paper/2211.09892/) (2022)
- [Source Identification in Abstractive Summarization](https://mlsanity.com/paper/2402.04677/) (2024)
- [CTISum: A New Benchmark Dataset For Cyber Threat Intelligence Summarization](https://mlsanity.com/paper/2408.06576/) (2024)
- [How Private are Language Models in Abstractive Summarization?](https://mlsanity.com/paper/2412.12040/) (2024)

## Abstract (arXiv)

When writing a summary, humans tend to choose content from one or two sentences and merge them into a single summary sentence. However, the mechanisms behind the selection of one or multiple source sentences remain poorly understood. Sentence fusion assumes multi-sentence input; yet sentence selection methods only work with single sentences and not combinations of them. There is thus a crucial gap between sentence selection and fusion to support summarizing by both compressing single sentences and fusing pairs. This paper attempts to bridge the gap by ranking sentence singletons and pairs together in a unified space. Our proposed framework attempts to model human methodology by selecting either a single sentence or a pair of sentences, then compressing or fusing the sentence(s) to produce a summary sentence. We conduct extensive experiments on both single- and multi-document summarization datasets and report findings on sentence selection and abstraction.