# Overview of the VLSP 2023 -- ComOM Shared Task: A Data Challenge for Comparative Opinion Mining from Vietnamese Product Reviews

arXiv: https://arxiv.org/abs/2402.13613 · HTML: https://mlsanity.com/paper/2402.13613/

## Key facts

- Cited by 3 papers in the map (data through 7 Oct 2026).
- Builds on 0 of its 9 references that are in the map.
- First posted on arXiv 21 Feb 2024.

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

- [GreenMind: A Next-Generation Vietnamese Large Language Model for Structured and Logical Reasoning](https://mlsanity.com/paper/2504.16832/) (2025)
- [XAI-enhanced Comparative Opinion Mining via Aspect-based Scoring and Semantic Reasoning](https://mlsanity.com/paper/2603.01212/) (2026)
- [Comparative Opinion Mining in Product Reviews: Multi-perspective Prompt-based Learning](https://mlsanity.com/paper/2412.08508/) (2024)

## Abstract (arXiv)

This paper presents a comprehensive overview of the Comparative Opinion Mining from Vietnamese Product Reviews shared task (ComOM), held as part of the 10th International Workshop on Vietnamese Language and Speech Processing (VLSP 2023). The primary objective of this shared task is to advance the field of natural language processing by developing techniques that proficiently extract comparative opinions from Vietnamese product reviews. Participants are challenged to propose models that adeptly extract a comparative "quintuple" from a comparative sentence, encompassing Subject, Object, Aspect, Predicate, and Comparison Type Label. We construct a human-annotated dataset comprising 120 documents, encompassing 7427 non-comparative sentences and 2468 comparisons within 1798 sentences. Participating models undergo evaluation and ranking based on the Exact match macro-averaged quintuple F1 score.