Summarizing Speech: A Comprehensive Survey
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915559599243264 |
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| author | Retkowski, Fabian Züfle, Maike Sudmann, Andreas Pfau, Dinah Watanabe, Shinji Niehues, Jan Waibel, Alexander |
| author_facet | Retkowski, Fabian Züfle, Maike Sudmann, Andreas Pfau, Dinah Watanabe, Shinji Niehues, Jan Waibel, Alexander |
| contents | Speech summarization has become an essential tool for efficiently managing and accessing the growing volume of spoken and audiovisual content. However, despite its increasing importance, speech summarization remains loosely defined. The field intersects with several research areas, including speech recognition, text summarization, and specific applications like meeting summarization. This survey not only examines existing datasets and evaluation protocols, which are crucial for assessing the quality of summarization approaches, but also synthesizes recent developments in the field, highlighting the shift from traditional systems to advanced models like fine-tuned cascaded architectures and end-to-end solutions. In doing so, we surface the ongoing challenges, such as the need for realistic evaluation benchmarks, multilingual datasets, and long-context handling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_08024 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Summarizing Speech: A Comprehensive Survey Retkowski, Fabian Züfle, Maike Sudmann, Andreas Pfau, Dinah Watanabe, Shinji Niehues, Jan Waibel, Alexander Computation and Language Sound Audio and Speech Processing Speech summarization has become an essential tool for efficiently managing and accessing the growing volume of spoken and audiovisual content. However, despite its increasing importance, speech summarization remains loosely defined. The field intersects with several research areas, including speech recognition, text summarization, and specific applications like meeting summarization. This survey not only examines existing datasets and evaluation protocols, which are crucial for assessing the quality of summarization approaches, but also synthesizes recent developments in the field, highlighting the shift from traditional systems to advanced models like fine-tuned cascaded architectures and end-to-end solutions. In doing so, we surface the ongoing challenges, such as the need for realistic evaluation benchmarks, multilingual datasets, and long-context handling. |
| title | Summarizing Speech: A Comprehensive Survey |
| topic | Computation and Language Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2504.08024 |