# Document Summarization with Text Segmentation

**Authors:** Lesly Miculicich, Benjamin Han  
**arXiv:** [2301.08817](https://arxiv.org/abs/2301.08817)  
**Date:** 2023-01-20  
**Summary page:** https://www.summarizepaper.com/en/paper/2301.08817v1/

## Key points

- The paper explores the use of text segmentation models to improve extractive summarization task.
- Two state-of-the-art models, one supervised and one unsupervised, are evaluated for their accuracy in segmenting scientific articles.
- Highly accurate segmentation method can significantly improve the quality of extractive summarization, particularly in documents where the most relevant information is not at the beginning.
- Various strategies for integrating segment data into the summarization model are discussed and the importance of segmentation model accuracy is highlighted.
- Previous studies have explored both unsupervised and supervised methods for text segmentation, with varying levels of success depending on factors such as domain and genre.
- Text segmentation has also been applied to automatic summarization, with some studies focusing on meeting transcripts and others incorporating information on document segments.
- This paper differs from previous work by training a segmentation model to predict sections and then integrating this information into an extractive summarization model.
- The paper is organized into different sections that provide an overview of related work, describe the text segmentation and summarization models used in this study, present the dataset and evaluation metrics used, report on experimental results, and conclude with a discussion of future work.
- Overall, this study highlights the potential benefits of using text segmentation models to improve extractive summarization performance in scientific articles.

## Summary

This paper explores the use of text segmentation models to improve the extractive summarization task. Two state-of-the-art models, one supervised and one unsupervised, are evaluated for their accuracy in segmenting scientific articles. The results show that using a highly accurate segmentation method can significantly improve the quality of extractive summarization, particularly in documents where the most relevant information is not at the beginning. The paper also discusses various strategies for integrating segment data into the summarization model and highlights the importance of segmentation model accuracy. In terms of related work, previous studies have explored both unsupervised and supervised methods for text segmentation, with varying levels of success depending on factors such as domain and genre. Text segmentation has also been applied to automatic summarization, with some studies focusing on meeting transcripts and others incorporating information on document segments. This paper differs from previous work by training a segmentation model to predict sections and then integrating this information into an extractive summarization model. The paper is organized as follows: Section 2 provides an overview of related work; Sections 3 and 4 describe the text segmentation and summarization models used in this study; Section 5 presents the dataset and evaluation metrics used; Section 6 reports on experimental results; finally, Section 7 concludes with a discussion of future work. Overall, this study highlights the potential benefits of using text segmentation models to improve extractive summarization performance in scientific articles.