# Team Enigma at ArgMining-EMNLP 2021: Leveraging Pre-trained Language Models for Key Point Matching

arXiv: https://arxiv.org/abs/2110.12370 · HTML: https://mlsanity.com/paper/2110.12370/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 16 references that are in the map.
- First posted on arXiv 24 Oct 2021.

## Builds on (most cited first, 9 of 9)

- [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)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [What Does BERT Look At? An Analysis of BERT's Attention](https://mlsanity.com/paper/1906.04341/) (2019)
- [SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation](https://mlsanity.com/paper/1708.00055/) (2017)
- [A Large-scale Dataset for Argument Quality Ranking: Construction and Analysis](https://mlsanity.com/paper/1911.11408/) (2019)
- [Multi-hop Inference for Sentence-level TextGraphs: How Challenging is Meaningfully Combining Information for Science Question Answering?](https://mlsanity.com/paper/1805.11267/) (2018)

## Built on by (most cited first, 11 of 11)

- [Prompted Aspect Key Point Analysis for Quantitative Review Summarization](https://mlsanity.com/paper/2407.14049/) (2024)
- [Do You Hear The People Sing? Key Point Analysis via Iterative Clustering and Abstractive Summarisation](https://mlsanity.com/paper/2305.16000/) (2023)
- [Exploring Key Point Analysis with Pairwise Generation and Graph Partitioning](https://mlsanity.com/paper/2404.11384/) (2024)
- [ArgCMV: An Argument Summarization Benchmark for the LLM-era](https://mlsanity.com/paper/2508.19580/) (2025)
- [Hi-ArG: Exploring the Integration of Hierarchical Argumentation Graphs in Language Pretraining](https://mlsanity.com/paper/2312.00874/) (2023)
- [Arguments to Key Points Mapping with Prompt-based Learning](https://mlsanity.com/paper/2211.14995/) (2022)
- [From Judgement's Premises Towards Key Points](https://mlsanity.com/paper/2212.12238/) (2022)
- [QQSUM: A Novel Task and Model of Quantitative Query-Focused Summarization for Review-based Product Question Answering](https://mlsanity.com/paper/2506.04020/) (2025)
- [Arg-LLaDA: Argument Summarization via Large Language Diffusion Models and Sufficiency-Aware Refinement](https://mlsanity.com/paper/2507.19081/) (2025)
- [ARQUSUMM: Argument-aware Quantitative Summarization of Online Conversations](https://mlsanity.com/paper/2511.16985/) (2025)
- [Key Point Analysis Needs Structure Recovery: Task Definition, Dataset Diagnosis, and a Structure-Aware Benchmark](https://mlsanity.com/paper/2608.25854/) (2026)

## Abstract (arXiv)

We present the system description for our submission towards the Key Point Analysis Shared Task at ArgMining 2021. Track 1 of the shared task requires participants to develop methods to predict the match score between each pair of arguments and keypoints, provided they belong to the same topic under the same stance. We leveraged existing state of the art pre-trained language models along with incorporating additional data and features extracted from the inputs (topics, key points, and arguments) to improve performance. We were able to achieve mAP strict and mAP relaxed score of 0.872 and 0.966 respectively in the evaluation phase, securing 5th place on the leaderboard. In the post evaluation phase, we achieved a mAP strict and mAP relaxed score of 0.921 and 0.982 respectively. All the codes to generate reproducible results on our models are available on Github.