Challenge: Prior work on labeling arguments extracted from peer review text has focused qualified labor force on labelling arguments extracted by the text.
Approach: They synthesize label sets from prior work and extend them to include fine-grained annotations of review and rebuttal sentences.
Outcome: The proposed dataset synthesizes label sets from prior work and extends them to include fine-grained annotation of review and rebuttal sentences.

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A Multi-layer Annotated Corpus of Argumentative Text: From Argument Schemes to Discourse Relations (L18-1)

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Challenge: Recent interest in Argumentation Mining has brought to the fore the need for corpora annotated with argument information, which can be used as training data.
Approach: They propose a set of guidelines for the annotation of argument schemes and a new annotation tool for the 'inferential' argument schemes.
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Exploring Jiu-Jitsu Argumentation for Writing Peer Review Rebuttals (2023.emnlp-main)

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Challenge: Recent work suggests that instead of directly countering surface-level reasoning, one should follow an argumentation style inspired by the Jiu-Jitsu “soft” combat system.
Approach: They propose a task of attitude and theme-guided rebuttal generation for peer reviews to enrich existing discourse structure with attitude roots, attitude themes, and canonical reversals.
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A Corpus for Argumentative Writing Support in German (2020.coling-main)

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Challenge: In today's world most information is readily available. Consequently, the sole reproduction of information is losing attention.
Approach: They propose an annotation approach to capture claims and premises of arguments and their relations in student-written peer reviews on business models in german language.
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Who Sides with Whom? Towards Computational Construction of Discourse Networks for Political Debates (P19-1)

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Challenge: a vision of computational construction of discourse networks from newspaper reports is essential for understanding democratic political decision making.
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A Dataset of General-Purpose Rebuttal (D19-1)

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Challenge: a key element in argumentation is rebuttal, the ability to contest an argument by presenting a counter-argument.
Approach: They propose a method based on general rebuttal arguments to produce a critical response to a long argumentative text.
Outcome: The proposed method overcomes the need for topic-specific arguments to be provided . it allows creating responses beyond the scope of topics for which specific arguments are available .
APE: Argument Pair Extraction from Peer Review and Rebuttal via Multi-task Learning (2020.emnlp-main)

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Challenge: Argument mining is an important research field that attracts growing attention in recent years.
Approach: They propose a new task to extract argument pairs from peer review and rebuttal . they use an open review platform to analyze the contents, structure and connections .
Outcome: The proposed task is based on a dataset of 4,764 fully annotated review-rebuttal passage pairs . it is able to detect argumentative propositions and extract argument pairs from the corpus .
ENTRUST: Argument Reframing with Language Models and Entailment (2021.naacl-main)

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Challenge: Public opinion has been shown to be significantly influenced by framing effects.
Approach: They propose a method for reframing arguments that combines controllable text generation with a post-decoding entailment component to achieve the same denotation.
Outcome: The proposed method is effective compared to baselines along the dimensions of fluency, meaning, and trustworthiness/reduction of fear.
Summarizing Multiple Documents with Conversational Structure for Meta-Review Generation (2023.findings-emnlp)

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Challenge: Existing models for abstractive text summarization do not provide explicit interdocument relationships among source documents.
Approach: They propose a model that uses sparse attention based on the conversational structure and a multi-task training objective that predicts metadata features.
Outcome: The proposed model outperforms baseline models in terms of evaluation metrics but struggle to handle conflicts in source documents.
QUD-Based Annotation of Discourse Structure and Information Structure: Tool and Evaluation (L18-1)

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Challenge: a new annotation scheme and discourse-analytic method is developed for information structure annotation.
Approach: They propose a new annotation scheme and a discourse-analytic method based on Questions under Discussion . they introduce a tool which enables the analyst to semi-automatically segment texts and enhance them with QUDs .
Outcome: The proposed method achieves good inter-annotator scores and good agreement with discourse annotations.
Argument Mining for Understanding Peer Reviews (N19-1)

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Challenge: In 2015 alone, approximately 63.4 million hours were spent on peer reviews.
Approach: They propose to automatically detect argumentative propositions put forward by reviewers and their types by automatically detecting their types and types.
Outcome: The proposed method detects (1) the argumentative propositions put forward by reviewers, and (2) their types (e.g., evaluating the work or making suggestions for improvement).

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