  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.
