# Document Summarization with Text Segmentation

arXiv: https://arxiv.org/abs/2301.08817 · HTML: https://mlsanity.com/paper/2301.08817/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 18 references that are in the map.
- First posted on arXiv 20 Jan 2023.

## Builds on (most cited first, 12 of 12)

- [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)
- [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)
- [DeltaLM: Encoder-Decoder Pre-training for Language Generation and Translation by Augmenting Pretrained Multilingual Encoders](https://mlsanity.com/paper/2106.13736/) (2021)
- [Topic-Aware Multi-turn Dialogue Modeling](https://mlsanity.com/paper/2009.12539/) (2020)
- [Topic-aware Pointer-Generator Networks for Summarizing Spoken Conversations](https://mlsanity.com/paper/1910.01335/) (2019)
- [Unsupervised Topic Segmentation of Meetings with BERT Embeddings](https://mlsanity.com/paper/2106.12978/) (2021)
- [HiStruct+: Improving Extractive Text Summarization with Hierarchical Structure Information](https://mlsanity.com/paper/2203.09629/) (2022)
- [Countering the Effects of Lead Bias in News Summarization via Multi-Stage Training and Auxiliary Losses](https://mlsanity.com/paper/1909.04028/) (2019)
- [End-to-End Segmentation-based News Summarization](https://mlsanity.com/paper/2110.07850/) (2021)
- [Sequence Model with Self-Adaptive Sliding Window for Efficient Spoken Document Segmentation](https://mlsanity.com/paper/2107.09278/) (2021)

## Built on by (most cited first, 3 of 3)

- [LoRaLay: A Multilingual and Multimodal Dataset for Long Range and Layout-Aware Summarization](https://mlsanity.com/paper/2301.11312/) (2023)
- [TocBERT: Medical Document Structure Extraction Using Bidirectional Transformers](https://mlsanity.com/paper/2406.19526/) (2024)
- [BP-Seg: A graphical model approach to unsupervised and non-contiguous text segmentation using belief propagation](https://mlsanity.com/paper/2505.16965/) (2025)

## Abstract (arXiv)

In this paper, we exploit the innate document segment structure for improving the extractive summarization task. We build two text segmentation models and find the most optimal strategy to introduce their output predictions in an extractive summarization model. Experimental results on a corpus of scientific articles show that extractive summarization benefits from using a highly accurate segmentation method. In particular, most of the improvement is in documents where the most relevant information is not at the beginning thus, we conclude that segmentation helps in reducing the lead bias problem.