Objective Argument Summarization in Search

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

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  • Universität Leipzig
  • Zentrum für skalierbare Datenanalyse und Künstliche Intelligenz Dresden/Leipzig (ScaDS.AI)
  • Universität Kassel
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Details

OriginalspracheEnglisch
Titel des SammelwerksRobust Argumentation Machines - First International Conference, RATIO 2024, Proceedings
UntertitelFirst International Conference, RATIO 2024, Bielefeld, Germany, June 5–7, 2024, Proceedings
Herausgeber/-innenPhilipp Cimiano, Anette Frank, Michael Kohlhase, Benno Stein
ErscheinungsortCham
Seiten335-351
Seitenumfang17
Auflage1.
ISBN (elektronisch)978-3-031-63536-6
PublikationsstatusVeröffentlicht - 17 Juli 2024
Veranstaltung1st International Conference on Recent Advances in Robust Argumentation Machines (RATIO-24) - Bielefeld, Deutschland
Dauer: 5 Juni 20247 Juni 2024

Publikationsreihe

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band14638 LNAI
ISSN (Print)0302-9743
ISSN (elektronisch)1611-3349

Abstract

Decision-making and opinion formation are influenced by arguments from various online sources, including social media, web publishers, and, not least, the search engines used to retrieve them. However, many, if not most, arguments on the web are informal, especially in online discussions or on personal pages. They can be long and unstructured, subjective and emotional, and contain inappropriate language. This makes it difficult to find relevant arguments efficiently. We hypothesize that, on search engine results pages,“objective snippets” of arguments are better suited than the commonly used extractive snippets and develop corresponding methods for two important tasks: snippet generation and neutralization. For each of these tasks, we investigate two approaches based on (1) prompt engineering for large language models (LLMs), and (2) supervised models trained on existing datasets. We find that a supervised summarization model outperforms zero-shot summarization with LLMs for snippet generation. For neutralization, using reinforcement learning to align an LLM with human preferences for suitable arguments leads to the best results. Both tasks are complementary, and their combination leads to the best snippets of arguments according to automatic and human evaluation.

ASJC Scopus Sachgebiete

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Objective Argument Summarization in Search. / Ziegenbein, Timon; Syed, Shahbaz; Potthast, Martin et al.
Robust Argumentation Machines - First International Conference, RATIO 2024, Proceedings: First International Conference, RATIO 2024, Bielefeld, Germany, June 5–7, 2024, Proceedings. Hrsg. / Philipp Cimiano; Anette Frank; Michael Kohlhase; Benno Stein. 1. Aufl. Cham, 2024. S. 335-351 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 14638 LNAI).

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

Ziegenbein, T, Syed, S, Potthast, M & Wachsmuth, H 2024, Objective Argument Summarization in Search. in P Cimiano, A Frank, M Kohlhase & B Stein (Hrsg.), Robust Argumentation Machines - First International Conference, RATIO 2024, Proceedings: First International Conference, RATIO 2024, Bielefeld, Germany, June 5–7, 2024, Proceedings. 1. Aufl., Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bd. 14638 LNAI, Cham, S. 335-351, 1st International Conference on Recent Advances in Robust Argumentation Machines (RATIO-24), Bielefeld, Deutschland, 5 Juni 2024. https://doi.org/10.1007/978-3-031-63536-6_20
Ziegenbein, T., Syed, S., Potthast, M., & Wachsmuth, H. (2024). Objective Argument Summarization in Search. In P. Cimiano, A. Frank, M. Kohlhase, & B. Stein (Hrsg.), Robust Argumentation Machines - First International Conference, RATIO 2024, Proceedings: First International Conference, RATIO 2024, Bielefeld, Germany, June 5–7, 2024, Proceedings (1. Aufl., S. 335-351). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 14638 LNAI).. https://doi.org/10.1007/978-3-031-63536-6_20
Ziegenbein T, Syed S, Potthast M, Wachsmuth H. Objective Argument Summarization in Search. in Cimiano P, Frank A, Kohlhase M, Stein B, Hrsg., Robust Argumentation Machines - First International Conference, RATIO 2024, Proceedings: First International Conference, RATIO 2024, Bielefeld, Germany, June 5–7, 2024, Proceedings. 1. Aufl. Cham. 2024. S. 335-351. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-63536-6_20
Ziegenbein, Timon ; Syed, Shahbaz ; Potthast, Martin et al. / Objective Argument Summarization in Search. Robust Argumentation Machines - First International Conference, RATIO 2024, Proceedings: First International Conference, RATIO 2024, Bielefeld, Germany, June 5–7, 2024, Proceedings. Hrsg. / Philipp Cimiano ; Anette Frank ; Michael Kohlhase ; Benno Stein. 1. Aufl. Cham, 2024. S. 335-351 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Download
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abstract = "Decision-making and opinion formation are influenced by arguments from various online sources, including social media, web publishers, and, not least, the search engines used to retrieve them. However, many, if not most, arguments on the web are informal, especially in online discussions or on personal pages. They can be long and unstructured, subjective and emotional, and contain inappropriate language. This makes it difficult to find relevant arguments efficiently. We hypothesize that, on search engine results pages,“objective snippets” of arguments are better suited than the commonly used extractive snippets and develop corresponding methods for two important tasks: snippet generation and neutralization. For each of these tasks, we investigate two approaches based on (1) prompt engineering for large language models (LLMs), and (2) supervised models trained on existing datasets. We find that a supervised summarization model outperforms zero-shot summarization with LLMs for snippet generation. For neutralization, using reinforcement learning to align an LLM with human preferences for suitable arguments leads to the best results. Both tasks are complementary, and their combination leads to the best snippets of arguments according to automatic and human evaluation.",
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AU - Ziegenbein, Timon

AU - Syed, Shahbaz

AU - Potthast, Martin

AU - Wachsmuth, Henning

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PY - 2024/7/17

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N2 - Decision-making and opinion formation are influenced by arguments from various online sources, including social media, web publishers, and, not least, the search engines used to retrieve them. However, many, if not most, arguments on the web are informal, especially in online discussions or on personal pages. They can be long and unstructured, subjective and emotional, and contain inappropriate language. This makes it difficult to find relevant arguments efficiently. We hypothesize that, on search engine results pages,“objective snippets” of arguments are better suited than the commonly used extractive snippets and develop corresponding methods for two important tasks: snippet generation and neutralization. For each of these tasks, we investigate two approaches based on (1) prompt engineering for large language models (LLMs), and (2) supervised models trained on existing datasets. We find that a supervised summarization model outperforms zero-shot summarization with LLMs for snippet generation. For neutralization, using reinforcement learning to align an LLM with human preferences for suitable arguments leads to the best results. Both tasks are complementary, and their combination leads to the best snippets of arguments according to automatic and human evaluation.

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