# A Vietnamese Dataset for Text Segmentation and Multiple Choices Reading Comprehension

arXiv: https://arxiv.org/abs/2506.15978 · HTML: https://mlsanity.com/paper/2506.15978/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 40 references that are in the map.
- First posted on arXiv 19 Jun 2025.

## Builds on (most cited first, 25 of 25)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [RACE: Large-scale ReAding Comprehension Dataset From Examinations](https://mlsanity.com/paper/1704.04683/) (2017)
- [A Primer in BERTology: What we know about how BERT works](https://mlsanity.com/paper/2002.12327/) (2020)
- [CoQA: A Conversational Question Answering Challenge](https://mlsanity.com/paper/1808.07042/) (2018)
- [MLQA: Evaluating Cross-lingual Extractive Question Answering](https://mlsanity.com/paper/1910.07475/) (2019)
- [Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning](https://mlsanity.com/paper/1909.00277/) (2019)
- [PhoBERT: Pre-trained language models for Vietnamese](https://mlsanity.com/paper/2003.00744/) (2020)
- [Text Segmentation as a Supervised Learning Task](https://mlsanity.com/paper/1803.09337/) (2018)
- [Extractive Summarization of Long Documents by Combining Global and Local Context](https://mlsanity.com/paper/1909.08089/) (2019)
- [VnCoreNLP: A Vietnamese Natural Language Processing Toolkit](https://mlsanity.com/paper/1801.01331/) (2018)
- [SECTOR: A Neural Model for Coherent Topic Segmentation and Classification](https://mlsanity.com/paper/1902.04793/) (2019)
- [Rethinking Text Segmentation: A Novel Dataset and A Text-Specific Refinement Approach](https://mlsanity.com/paper/2011.14021/) (2020)
- [A Vietnamese Dataset for Evaluating Machine Reading Comprehension](https://mlsanity.com/paper/2009.14725/) (2020)
- [Improving Sequence Tagging for Vietnamese Text Using Transformer-based Neural Models](https://mlsanity.com/paper/2006.15994/) (2020)
- [Transformer over Pre-trained Transformer for Neural Text Segmentation with Enhanced Topic Coherence](https://mlsanity.com/paper/2110.07160/) (2021)
- [End-to-End Segmentation-based News Summarization](https://mlsanity.com/paper/2110.07850/) (2021)
- [Improving Long Document Topic Segmentation Models With Enhanced Coherence Modeling](https://mlsanity.com/paper/2310.11772/) (2023)
- [ViDeBERTa: A powerful pre-trained language model for Vietnamese](https://mlsanity.com/paper/2301.10439/) (2023)
- [Conversational Machine Reading Comprehension for Vietnamese Healthcare Texts](https://mlsanity.com/paper/2105.01542/) (2021)
- [From Word Segmentation to POS Tagging for Vietnamese](https://mlsanity.com/paper/1711.04951/) (2017)
- [NLPBK at VLSP-2020 shared task: Compose transformer pretrained models for Reliable Intelligence Identification on Social network](https://mlsanity.com/paper/2101.12672/) (2021)
- [Using Large Language Models for education managements in Vietnamese with low resources](https://mlsanity.com/paper/2501.15022/) (2025)

## Abstract (arXiv)

Vietnamese, the 20th most spoken language with over 102 million native speakers, lacks robust resources for key natural language processing tasks such as text segmentation and machine reading comprehension (MRC). To address this gap, we present VSMRC, the Vietnamese Text Segmentation and Multiple-Choice Reading Comprehension Dataset. Sourced from Vietnamese Wikipedia, our dataset includes 15,942 documents for text segmentation and 16,347 synthetic multiple-choice question-answer pairs generated with human quality assurance, ensuring a reliable and diverse resource. Experiments show that mBERT consistently outperforms monolingual models on both tasks, achieving an accuracy of 88.01% on MRC test set and an F1 score of 63.15% on text segmentation test set. Our analysis reveals that multilingual models excel in NLP tasks for Vietnamese, suggesting potential applications to other under-resourced languages. VSMRC is available at HuggingFace