ArgumenText: Searching for Arguments in Heterogeneous Sources

@inproceedings{Stab2018ArgumenTextSF,
  title={ArgumenText: Searching for Arguments in Heterogeneous Sources},
  author={Christian Stab and Johannes Daxenberger and Chris Stahlhut and Tristan Miller and Benjamin Schiller and Christopher Tauchmann and Steffen Eger and Iryna Gurevych},
  booktitle={NAACL},
  year={2018}
}
Argument mining is a core technology for enabling argument search in large corpora. However, most current approaches fall short when applied to heterogeneous texts. In this paper, we present an argument retrieval system capable of retrieving sentential arguments for any given controversial topic. By analyzing the highest-ranked results extracted from Web sources, we found that our system covers 89% of arguments found in expert-curated lists of arguments from an online debate portal, and also… 
View on ACL
aclanthology.org
80 Citations
Highly Influential Citations
6
Background Citations
48
Methods Citations
26
Results Citations
4

Figures and Tables from this paper

80 Citations

Citation Type

Citation Type

Creating an Argument Search Engine for Online Debates
TLDR
This work presents an argument search engine for the argsme corpus, focusing on preprocessing the corpus while also addressing the heterogeneity problem and implementing a query expansion feature.
Towards Full-Fledged Argument Search: A Framework for Extracting and Clustering Arguments from Unstructured Text
TLDR
This paper proposes a framework to combine the keyword search with precomputed topic clusters for argument-query matching, to apply a novel approach based on sentence-level sequence-labeling for argument identification, and to present aggregated arguments to users based on topic-aware argument clustering.
Overview of Touché 2021: Argument Retrieval - Extended Abstract
TLDR
This paper organizes the Touché lab on argument retrieval with two shared tasks: (1) argument retrieval for controversial questions, where arguments are to be retrieved from a focused debate portal-based collection and, (2) argument retrieved for comparative Questions, where argumentative documents are to been retrieved fromA generic web crawl.
Overview of Touché 2021: Argument Retrieval
TLDR
This paper organizes the Touché lab on argument retrieval with two shared tasks: (1) argument retrieval for controversial questions, where arguments are to be retrieved from a focused debate portal-based collection and, (2) argument retrieved for comparative Questions, where argumentative documents are to been retrieved fromA generic web crawl.
Quality-Aware Ranking of Arguments
TLDR
This work presents a quality-aware ranking framework for arguments already extracted from texts and represented as argument graphs, considering multiple established quality measures.
ArgumenText: Argument Classification and Clustering in a Generalized Search Scenario
TLDR
This contribution outlines the major technology-related challenges and proposed solutions for the tasks of argument extraction from heterogeneous sources and argument clustering and lays out exemplary industry applications and remaining challenges.
Data Acquisition for Argument Search: The args.me Corpus
TLDR
This paper presents the new corpus of the argument search engine args.me, which follows the former paradigm, and freely provides the corpus to the community, which is one of the largest argument resources available so far.
Fine and Coarse Granular Argument Classification before Clustering
TLDR
A novel pipeline for clustering arguments is introduced and a new dataset consisting of 365 argument graphs accompanying more than 11,000 high-quality arguments that, contrary to previous datasets, have been generated, displayed, and verified by journalists and were published in newspapers are introduced.
TACAM: Topic And Context Aware Argument Mining
TLDR
This work argues that topic information is crucial for argument mining, since the topic defines the semantic context of an argument, and proposes different models for the classification of arguments, which take information about a topic of a argument into account.
Cross-topic Argument Mining from Heterogeneous Sources
TLDR
This paper proposes a new sentential annotation scheme that is reliably applicable by crowd workers to arbitrary Web texts and shows that integrating topic information into bidirectional long short-term memory networks outperforms vanilla BiLSTMs in F1 in two- and three-label cross-topic settings.
...
...

References

SHOWING 1-10 OF 15 REFERENCES
MARGOT: A web server for argumentation mining
Building an Argument Search Engine for the Web
TLDR
An argument search framework for studying how people query for arguments, how to mine arguments from the web, or how to rank them is developed and a prototype search engine is built that relies on an initial, freely accessible index of nearly 300k arguments crawled from reliable web resources.
Context-Independent Claim Detection for Argument Mining
TLDR
This work proposes a method that exploits structured parsing information to detect claims without resorting to contextual information, and yet achieve a performance comparable to that of state-of-the-art methods that heavily rely on the context.
Cross-topic Argument Mining from Heterogeneous Sources Using Attention-based Neural Networks
TLDR
A new sentential annotation scheme that is reliably applicable by crowd workers to arbitrary Web texts that generalizes best to unseen topics and outperforms vanilla BiLSTM models by 6% in accuracy and 11% in F-score is proposed.
Argumentation mining: the detection, classification and structure of arguments in text
TLDR
This paper analyzes the main research questions when dealing with argumentation mining and the different methods studied and developed in order to successfully confront the challenges of argumentationmining in legal texts.
Unit Segmentation of Argumentative Texts
TLDR
This paper studies the major parameters of unit segmentation systematically, and explores the effectiveness of various features, when capturing words separately, along with their neighbors, or even along with the entire text.
Neural End-to-End Learning for Computational Argumentation Mining
TLDR
This work investigates neural techniques for end-to-end computational argumentation mining and finds that jointly learning 'natural' subtasks, in a multi-task learning setup, improves performance.
Okapi at TREC
TLDR
Much of the work involved investigating plausible methods of applying Okapi-style weighting to phrases, and expansion using terms from the top documents retrieved by a pilot search on topic terms was used.
A broad-coverage collection of portable NLP components for building shareable analysis pipelines
Due to the diversity of natural language processing (NLP) tools and resources, combining them into processing pipelines is an important issue, and sharing these pipelines with others remains a…
C4Corpus: Multilingual Web-size Corpus with Free License
TLDR
This article presents the construction of 12 million-pages Web corpus (over 10 billion tokens) licensed under CreativeCommons license family in 50+ languages that has been extracted from CommonCrawl, the largest publicly available general Web crawl to date with about 2 billion crawled URLs.
...
...