# Liputan6: A Large-scale Indonesian Dataset for Text Summarization

arXiv: https://arxiv.org/abs/2011.00679 · HTML: https://mlsanity.com/paper/2011.00679/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2020.

## Built on by (most cited first, 14 of 14)

- [A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methods](https://mlsanity.com/paper/2403.02901/) (2024)
- [One Country, 700+ Languages: NLP Challenges for Underrepresented Languages and Dialects in Indonesia](https://mlsanity.com/paper/2203.13357/) (2022)
- [IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generation](https://mlsanity.com/paper/2104.08200/) (2021)
- [Large Language Models Only Pass Primary School Exams in Indonesia: A Comprehensive Test on IndoMMLU](https://mlsanity.com/paper/2310.04928/) (2023)
- [NusaCrowd: Open Source Initiative for Indonesian NLP Resources](https://mlsanity.com/paper/2212.09648/) (2022)
- [IndoBERTweet: A Pretrained Language Model for Indonesian Twitter with Effective Domain-Specific Vocabulary Initialization](https://mlsanity.com/paper/2109.04607/) (2021)
- [Evaluating the Efficacy of Summarization Evaluation across Languages](https://mlsanity.com/paper/2106.01478/) (2021)
- [BEAMetrics: A Benchmark for Language Generation Evaluation Evaluation](https://mlsanity.com/paper/2110.09147/) (2021)
- [LR-Sum: Summarization for Less-Resourced Languages](https://mlsanity.com/paper/2212.09674/) (2022)
- [The State and Fate of Summarization Datasets: A Survey](https://mlsanity.com/paper/2411.04585/) (2024)
- [Investigating Text Shortening Strategy in BERT: Truncation vs Summarization](https://mlsanity.com/paper/2403.12799/) (2024)
- [SEA-BED: How Do Embedding Models Represent Southeast Asian Languages?](https://mlsanity.com/paper/2508.12243/) (2025)
- [IndoPref: A Multi-Domain Pairwise Preference Dataset for Indonesian](https://mlsanity.com/paper/2507.22159/) (2025)
- [BOE-XSUM: Extreme Summarization in Clear Language of Spanish Legal Decrees and Notifications](https://mlsanity.com/paper/2509.24908/) (2025)

## Abstract (arXiv)

In this paper, we introduce a large-scale Indonesian summarization dataset. We harvest articles from Liputan6.com, an online news portal, and obtain 215,827 document-summary pairs. We leverage pre-trained language models to develop benchmark extractive and abstractive summarization methods over the dataset with multilingual and monolingual BERT-based models. We include a thorough error analysis by examining machine-generated summaries that have low ROUGE scores, and expose both issues with ROUGE it-self, as well as with extractive and abstractive summarization models.