This document summarizes research presented at recent Argument Mining workshops. It describes tasks from 2021 and 2022 on key point analysis and validity-novelty prediction. It also outlines papers on extracting argument components, predicting reasoning markers, and detecting argument boundaries. The document indicates that argument mining can analyze debates on social media and that legal corpora can help develop argument mining systems.

1. Argument Mining
2023.08.01

2. Contents
• Argument Mining
• Workshop on Argument Mining
 Argument Mining Procedure
 2021 Task : Key Point Analysis
 2022 Task : Validity-Novelty Prediction
 Other papars
• Development of Argument Mining

3. 2
Argument Mining
[1] Chen et al., “Analyzing Culture-Specific Argument Structures in Learner Essays,” ACL ArgMining 2022
[2] Nguyen et al., “Extracting Argument and Domain Words for Identifying Argument Components in Texts,” ACL ArgMining 2015
• What is Argument Mining?

4. 3
Argument Mining
[1] Stab et al., “Parsing Argumentation Structures in Persuasive Essays” ACL’17
• Argumentation Structures

5. 4
Argument Mining
[1] Sofi et al., “A Robustness Evaluation Framework for Argument Mining” ACL ArgMining 2022
• Argument Mining

6. 5
Argument Mining Workshop
[1] Nguyen et al., “Extracting Argument and Domain Words for Identifying Argument Components in Texts” ACL ArgMining 2015
• Nguyen et al., “Extracting Argument and Domain Words for Identifying
Argument Components in Texts,” ACL ArgMining 2015
LDA : To learn argument and domain words
• Lexical aspect : topic model
 development data (described below) to separate argument words (e.g. ‘view’, ‘conclude’, ‘think’)
from domain words (e.g. ‘art’, ‘life’) <-> n-gram
• Structural aspect : subject and main verb
 use dependency parses to extract pairs of subject and main verb of sentences, e.g. “I.think”,
“view.be”

7. 6
Argument Mining Workshop
[1] Clayton et al., “Predicting the Presence of Reasoning Markers in Argumentative Text” ACL ArgMining 2022
• Clayton et al., “Predicting the Presence of Reasoning Markers in Argumentative
Text” ACL ArgMining 2022

8. 7
Argument Mining Workshop
[1] Clayton et al., “Predicting the Presence of Reasoning Markers in Argumentative Text” ACL ArgMining 2022
• Clayton et al., “Predicting the Presence of Reasoning Markers in Argumentative
Text” ACL ArgMining 2022
Reasoning Marker Prediction
Argument Component
Input AC types: “[premise] premise text [RM]
[claim] claim text“
RMs such as “in conclusion" are very
common in the dataset before major claims.

9. 8
Argument Mining 2021 Task
[1] Alshomary et al., “Key Point Analysis via Contrastive Learning and Extractive Argument Summarization” ACL ArgMining 2021
• KPA(Key Point Analysis)
: Key Point Analysis (KPA) is a new NLP task, with strong relations to Computational Argumentation, Opinion
Analysis, and Summarization

10. 9
Workshop on Argument Mining 2021
[1] Bhatti et al., “Argument Mining on Twitter: A Case Study on the Planned Parenthood Debate” ACL ArgMining 2021
• Bhatti et al., “Argument Mining on Twitter: A Case Study on the Planned
Parenthood Debate” ACL ArgMining 2021
contribution : It is possible to distinguish between arguments and reasons for short articles
such as Twitter. Public opinion on the SNS becomes clear.
• Support with reason
 : support the claim
• Support without reason
 : support the claim. But not provide a reason
• No explicit support
 :the user has a neutral or unclear stance

11. 10
Workshop on Argument Mining 2021
[1] Bhatti et al., “Argument Mining on Twitter: A Case Study on the Planned Parenthood Debate” ACL ArgMining 2021
• Bhatti et al., “Argument Mining on Twitter: A Case Study on the Planned
Parenthood Debate” ACL ArgMining 2021

12. 11
Argument Mining 2022 Task
[1] Heinisch et al., “Data Augmentation for Improving the Prediction of Validity and Novelty of Argumentative Conclusions” ACL ArgMining 2022
• PREDICTING THE VALIDITY AND NOVELTY OF ARGUMENTS

13. 12
Workshop on Argument Mining 2022
[1] Ruckdeschel et al. “Boundary Detection and Categorization of Argument Aspects via Supervised Learning” ArgMining 2022
• Ruckdeschel et al. “Boundary Detection and Categorization of Argument
Aspects via Supervised Learning” ArgMining 2022
Contribution : Clearly Argumentation which context unit aspects should be considered for
categorization of arguments (token, chunked, and sentence-level)
• Sequence Tagging
 Named Entity Recognition(NER)
• Chunk Normalization
 Syntactic Chunker
• Multi-Class Chunk Classification
 Entire Chunk, separated with [SEP] token
• Sentence Classification

14. 13
Workshop on Argument Mining 2022
[1] Ruckdeschel et al. “Boundary Detection and Categorization of Argument Aspects via Supervised Learning” ArgMining 2022
• Ruckdeschel et al. “Boundary Detection and Categorization of Argument
Aspects via Supervised Learning” ArgMining 2022

15. 14
Argument Mining Workshop
[1] Poudyal et al., “ECHR: Legal Corpus for Argument Mining” ACL ArgMining 2020
• Poudyal et al., “ECHR: Legal Corpus for Argument Mining” ACL ArgMining
2020
Proceed with Argument Mining using BERT
The learning process is the same as before
Provide visualization tools in user text
-> Efficient understanding of Argument

16. 15
Development of Argument Mining
[1] https://argmining-org.github.io/2023/
• Argument Mining 2023 Task