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

• URL: http://arxiv.org/abs/2112.00160v1
• Date: Tue, 30 Nov 2021 23:05:05 GMT
• Title: Towards Full-Fledged Argument Search: A Framework for Extracting and Clustering Arguments from Unstructured Text
• Authors: Michael F\"arber, Anna Steyer
• Abstract summary: Argument search aims at identifying arguments in natural language texts. Existing frameworks address only specific components of argument search. We propose a framework for addressing these shortcomings.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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