A Cascaded Architecture for Extractive Summarization of Multimedia Content via Audio-to-Text Alignment
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909572030005248 |
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| author | Hossain, Tanzir Islam, Ar-Rafi Hossain, Md. Sabbir Rasel, Annajiat Alim |
| author_facet | Hossain, Tanzir Islam, Ar-Rafi Hossain, Md. Sabbir Rasel, Annajiat Alim |
| contents | This study presents a cascaded architecture for extractive summarization of multimedia content via audio-to-text alignment. The proposed framework addresses the challenge of extracting key insights from multimedia sources like YouTube videos. It integrates audio-to-text conversion using Microsoft Azure Speech with advanced extractive summarization models, including Whisper, Pegasus, and Facebook BART XSum. The system employs tools such as Pytube, Pydub, and SpeechRecognition for content retrieval, audio extraction, and transcription. Linguistic analysis is enhanced through named entity recognition and semantic role labeling. Evaluation using ROUGE and F1 scores demonstrates that the cascaded architecture outperforms conventional summarization methods, despite challenges like transcription errors. Future improvements may include model fine-tuning and real-time processing. This study contributes to multimedia summarization by improving information retrieval, accessibility, and user experience. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_06275 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Cascaded Architecture for Extractive Summarization of Multimedia Content via Audio-to-Text Alignment Hossain, Tanzir Islam, Ar-Rafi Hossain, Md. Sabbir Rasel, Annajiat Alim Information Retrieval Artificial Intelligence Sound Audio and Speech Processing This study presents a cascaded architecture for extractive summarization of multimedia content via audio-to-text alignment. The proposed framework addresses the challenge of extracting key insights from multimedia sources like YouTube videos. It integrates audio-to-text conversion using Microsoft Azure Speech with advanced extractive summarization models, including Whisper, Pegasus, and Facebook BART XSum. The system employs tools such as Pytube, Pydub, and SpeechRecognition for content retrieval, audio extraction, and transcription. Linguistic analysis is enhanced through named entity recognition and semantic role labeling. Evaluation using ROUGE and F1 scores demonstrates that the cascaded architecture outperforms conventional summarization methods, despite challenges like transcription errors. Future improvements may include model fine-tuning and real-time processing. This study contributes to multimedia summarization by improving information retrieval, accessibility, and user experience. |
| title | A Cascaded Architecture for Extractive Summarization of Multimedia Content via Audio-to-Text Alignment |
| topic | Information Retrieval Artificial Intelligence Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2504.06275 |