Papers by Yoav Kantor

10 papers
From Arguments to Key Points: Towards Automatic Argument Summarization (2020.acl-main)

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Challenge: Recent work on topic-related argument mining has made it difficult to read and digest large amounts of information.
Approach: They propose to represent arguments as a small set of talking points, termed key points, each scored according to its salience.
Outcome: The proposed method can predict key points in advance, and it performs well.
Welcome to the Real World: Efficient, Incremental and Scalable Key Point Analysis (2023.emnlp-industry)

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Challenge: Key Point Analysis (KPA) extracts the main points from opinions and quantifies their prevalence.
Approach: They propose a key point analysis framework that extracts the main points from opinions and quantifies their prevalence.
Outcome: The proposed system is able to match sentences to key points over five datasets and demonstrate its performance.
Quantitative argument summarization and beyond: Cross-domain key point analysis (2020.emnlp-main)

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Challenge: Recent work on multi-document summarization lacks quantitative aspect of summarizing views, arguments or opinions . authors develop method for automatic extraction of key points, which is comparable to a human expert .
Approach: They propose to map arguments to a small set of expert-generated key points . they demonstrate that the applicability of key point analysis goes well beyond argumentation data .
Outcome: The proposed method outperforms arguments in municipal surveys and user reviews . it is shown that the extraction of key points is comparable to a human expert .
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 .
Listening Comprehension over Argumentative Content (D18-1)

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Challenge: In argumentation domain, people are exposed directly to audio (or the video), without access to a written version.
Approach: They present a task for machine listening comprehension in the argumentation domain and a dataset in English.
Outcome: The proposed task is based on 200 speeches arguing for or against 50 controversial topics and uses baseline methods to address it.
Semantic Relatedness of Wikipedia Concepts – Benchmark Data and a Working Solution (L18-1)

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Challenge: Existing methods to measure relatedness between Wikipedia concepts are lacking.
Approach: They propose a new type of concept relatedness dataset, WORD, which is annotated by a human . they use this dataset to assess relatedness between Wikipedia concepts using supervised methods.
Outcome: The proposed dataset outperforms existing methods for measuring relatedness between Wikipedia concepts.
From Key Points to Key Point Hierarchy: Structured and Expressive Opinion Summarization (2023.acl-long)

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Challenge: Key Point Analysis (KPA) is a new method for analyzing textual comments . it uses a list of concise sentences or phrases to extract key points from data .
Approach: They propose to organize key points into a hierarchy according to their specificity . they compare methods for predicting pairwise relations between key points .
Outcome: The proposed method improves on predicting pairwise key point relations and weak supervision.
Project Debater APIs: Decomposing the AI Grand Challenge (2021.emnlp-demo)

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Challenge: Project Debater is the first AI system that can debate human experts on complex topics.
Approach: They describe Project Debater's architecture and evaluate its performance . they will focus on Key Point Analysis, a novel technology that identifies main points .
Outcome: The proposed system can debate human experts on complex topics.
Every Bite Is an Experience: Key Point Analysis of Business Reviews (2021.acl-long)

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Challenge: Existing methods for summarizing reviews focus on measuring sentiment toward aspects of the review . recent work shows that KPA improves performance without domain-specific annotation .
Approach: They propose a framework that provides both textual and quantitative summary of the main points in review data.
Outcome: The proposed framework significantly improves on existing methods without annotations and human supervision.
CHAMP: Efficient Annotation and Consolidation of Cluster Hierarchies (2023.emnlp-demo)

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Challenge: Various annotation tasks require a complex hierarchical structure over nodes, where each node is a cluster of items.
Approach: They propose an open source tool that incrementally constructs clusters and hierarchy simultaneously over any type of text.
Outcome: The proposed approach significantly reduces annotation time and guarantees transitivity at the cluster and hierarchy levels.

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