# Extending Multi-Text Sentence Fusion Resources via Pyramid Annotations

arXiv: https://arxiv.org/abs/2110.04517 · HTML: https://mlsanity.com/paper/2110.04517/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 9 Oct 2021.

## Builds on (most cited first, 4 of 4)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Scoring Sentence Singletons and Pairs for Abstractive Summarization](https://mlsanity.com/paper/1906.00077/) (2019)
- [DiscoFuse: A Large-Scale Dataset for Discourse-Based Sentence Fusion](https://mlsanity.com/paper/1902.10526/) (2019)
- [Understanding Points of Correspondence between Sentences for Abstractive Summarization](https://mlsanity.com/paper/2006.05621/) (2020)

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

- [Summarization Programs: Interpretable Abstractive Summarization with Neural Modular Trees](https://mlsanity.com/paper/2209.10492/) (2022)
- [Towards Summary Candidates Fusion](https://mlsanity.com/paper/2210.08779/) (2022)
- [Bridging Continuous and Discrete Spaces: Interpretable Sentence Representation Learning via Compositional Operations](https://mlsanity.com/paper/2305.14599/) (2023)
- [GenSco: Can Question Decomposition based Passage Alignment improve Question Answering?](https://mlsanity.com/paper/2407.10245/) (2024)
- [Evaluating Whether LLMs Can Reliably Connect the DOTs?](https://mlsanity.com/paper/2609.38406/) (2026)

## Abstract (arXiv)

NLP models that compare or consolidate information across multiple documents often struggle when challenged with recognizing substantial information redundancies across the texts. For example, in multi-document summarization it is crucial to identify salient information across texts and then generate a non-redundant summary, while facing repeated and usually differently-phrased salient content. To facilitate researching such challenges, the sentence-level task of textitsentence fusion was proposed, yet previous datasets for this task were very limited in their size and scope. In this paper, we revisit and substantially extend previous dataset creation efforts. With careful modifications, relabeling and employing complementing data sources, we were able to triple the size of a notable earlier dataset. Moreover, we show that our extended version uses more representative texts for multi-document tasks and provides a larger and more diverse training set, which substantially improves model training.