SUMMHELPER: Collaborative Human-Computer Summarization

Aviv Slobodkin, Niv Nachum, Shmuel Amar, Ori Shapira, Ido Dagan

Research output: Contribution to conferencePaperpeer-review

Abstract

Current approaches for text summarization are predominantly automatic, with rather limited space for human intervention and control over the process. In this paper, we introduce SUMMHELPER,1 a 2-phase summarization assistant designed to foster human-machine collaboration. The initial phase involves content selection, where the system recommends potential content, allowing users to accept, modify, or introduce additional selections. The subsequent phase, content consolidation, involves SUMMHELPER generating a coherent summary from these selections, which users can then refine using visual mappings between the summary and the source text. Small-scale user studies reveal the effectiveness of our application, with participants being especially appreciative of the balance between automated guidance and opportunities for personal input.

Original languageEnglish
Pages554-565
Number of pages12
StatePublished - 2023
Event2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023 - Hybrid, Singapore, Singapore
Duration: 6 Dec 202310 Dec 2023

Conference

Conference2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023
Country/TerritorySingapore
CityHybrid, Singapore
Period6/12/2310/12/23

Bibliographical note

Publisher Copyright:
© 2023 Association for Computational Linguistics.

Funding

This work was supported by the Israel Science Foundation (grant no. 2827/21), and a grant from the Israel Ministry of Science and Technology. We would also like to thank Hadar Ronen for her guidance in planning the user studies.

FundersFunder number
Israel Science Foundation2827/21
Ministry of science and technology, Israel

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