# Understanding Points of Correspondence between Sentences for Abstractive Summarization

arXiv: https://arxiv.org/abs/2006.05621 · HTML: https://mlsanity.com/paper/2006.05621/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2020.

## Built on by (most cited first, 16 of 16)

- [Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks](https://mlsanity.com/paper/2204.07705/) (2022)
- [Toward Unifying Text Segmentation and Long Document Summarization](https://mlsanity.com/paper/2210.16422/) (2022)
- [QA-Align: Representing Cross-Text Content Overlap by Aligning Question-Answer Propositions](https://mlsanity.com/paper/2109.12655/) (2021)
- [DecipherPref: Analyzing Influential Factors in Human Preference Judgments via GPT-4](https://mlsanity.com/paper/2305.14702/) (2023)
- [Summarization Programs: Interpretable Abstractive Summarization with Neural Modular Trees](https://mlsanity.com/paper/2209.10492/) (2022)
- [Rephrase, Augment, Reason: Visual Grounding of Questions for Vision-Language Models](https://mlsanity.com/paper/2310.05861/) (2023)
- [Question-aware Transformer Models for Consumer Health Question Summarization](https://mlsanity.com/paper/2106.00219/) (2021)
- [iFacetSum: Coreference-based Interactive Faceted Summarization for Multi-Document Exploration](https://mlsanity.com/paper/2109.11621/) (2021)
- [Towards Abstractive Grounded Summarization of Podcast Transcripts](https://mlsanity.com/paper/2203.11425/) (2022)
- [Revisiting Sentence Union Generation as a Testbed for Text Consolidation](https://mlsanity.com/paper/2305.15605/) (2023)
- [Extending Multi-Text Sentence Fusion Resources via Pyramid Annotations](https://mlsanity.com/paper/2110.04517/) (2021)
- [Learning as Conversation: Dialogue Systems Reinforced for Information Acquisition](https://mlsanity.com/paper/2205.14748/) (2022)
- [GenerationPrograms: Fine-grained Attribution with Executable Programs](https://mlsanity.com/paper/2506.14580/) (2025)
- [Source Identification in Abstractive Summarization](https://mlsanity.com/paper/2402.04677/) (2024)
- [RISC: Generating Realistic Synthetic Bilingual Insurance Contract](https://mlsanity.com/paper/2304.04212/) (2023)
- [Learning Cluster Patterns for Abstractive Summarization](https://mlsanity.com/paper/2202.10967/) (2022)

## Abstract (arXiv)

Fusing sentences containing disparate content is a remarkable human ability that helps create informative and succinct summaries. Such a simple task for humans has remained challenging for modern abstractive summarizers, substantially restricting their applicability in real-world scenarios. In this paper, we present an investigation into fusing sentences drawn from a document by introducing the notion of points of correspondence, which are cohesive devices that tie any two sentences together into a coherent text. The types of points of correspondence are delineated by text cohesion theory, covering pronominal and nominal referencing, repetition and beyond. We create a dataset containing the documents, source and fusion sentences, and human annotations of points of correspondence between sentences. Our dataset bridges the gap between coreference resolution and summarization. It is publicly shared to serve as a basis for future work to measure the success of sentence fusion systems. (https://github.com/ucfnlp/points-of-correspondence)