@inproceedings{slobodkin-etal-2023-summhelper,
title = "{S}umm{H}elper: Collaborative Human-Computer Summarization",
author = "Slobodkin, Aviv and
Nachum, Niv and
Amar, Shmuel and
Shapira, Ori and
Dagan, Ido",
editor = "Feng, Yansong and
Lefever, Els",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-demo.50",
doi = "10.18653/v1/2023.emnlp-demo.50",
pages = "554--565",
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, and screencast demo at \url{https://www.youtube.com/watch?v=nGcknJwGhxk} 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.",
}
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<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, and screencast demo at https://www.youtube.com/watch?v=nGcknJwGhxk 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.</abstract>
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%0 Conference Proceedings
%T SummHelper: Collaborative Human-Computer Summarization
%A Slobodkin, Aviv
%A Nachum, Niv
%A Amar, Shmuel
%A Shapira, Ori
%A Dagan, Ido
%Y Feng, Yansong
%Y Lefever, Els
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F slobodkin-etal-2023-summhelper
%X 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, and screencast demo at https://www.youtube.com/watch?v=nGcknJwGhxk 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.
%R 10.18653/v1/2023.emnlp-demo.50
%U https://aclanthology.org/2023.emnlp-demo.50
%U https://doi.org/10.18653/v1/2023.emnlp-demo.50
%P 554-565
Markdown (Informal)
[SummHelper: Collaborative Human-Computer Summarization](https://aclanthology.org/2023.emnlp-demo.50) (Slobodkin et al., EMNLP 2023)
ACL
- Aviv Slobodkin, Niv Nachum, Shmuel Amar, Ori Shapira, and Ido Dagan. 2023. SummHelper: Collaborative Human-Computer Summarization. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 554–565, Singapore. Association for Computational Linguistics.