# Towards Full-Fledged Argument Search: A Framework for Extracting and Clustering Arguments from Unstructured Text

arXiv: https://arxiv.org/abs/2112.00160 · HTML: https://mlsanity.com/paper/2112.00160/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 28 references that are in the map.
- First posted on arXiv 30 Nov 2021.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [A Large-scale Dataset for Argument Quality Ranking: Construction and Analysis](https://mlsanity.com/paper/1911.11408/) (2019)
- [From Arguments to Key Points: Towards Automatic Argument Summarization](https://mlsanity.com/paper/2005.01619/) (2020)
- [Classification and Clustering of Arguments with Contextualized Word Embeddings](https://mlsanity.com/paper/1906.09821/) (2019)
- [SECTOR: A Neural Model for Coherent Topic Segmentation and Classification](https://mlsanity.com/paper/1902.04793/) (2019)
- [Neural End-to-End Learning for Computational Argumentation Mining](https://mlsanity.com/paper/1704.06104/) (2017)

## Abstract (arXiv)

Argument search aims at identifying arguments in natural language texts. In the past, this task has been addressed by a combination of keyword search and argument identification on the sentence- or document-level. However, existing frameworks often address only specific components of argument search and do not address the following aspects: (1) argument-query matching: identifying arguments that frame the topic slightly differently than the actual search query; (2) argument identification: identifying arguments that consist of multiple sentences; (3) argument clustering: selecting retrieved arguments by topical aspects. In this paper, we propose a framework for addressing these shortcomings. We suggest (1) to combine the keyword search with precomputed topic clusters for argument-query matching, (2) to apply a novel approach based on sentence-level sequence-labeling for argument identification, and (3) to present aggregated arguments to users based on topic-aware argument clustering. Our experiments on several real-world debate data sets demonstrate that density-based clustering algorithms, such as HDBSCAN, are particularly suitable for argument-query matching. With our sentence-level, BiLSTM-based sequence-labeling approach we achieve a macro F1 score of 0.71. Finally, evaluating our argument clustering method indicates that a fine-grained clustering of arguments by subtopics remains challenging but is worthwhile to be explored.