# A Cascaded Architecture for Extractive Summarization of Multimedia Content via Audio-to-Text Alignment

arXiv: https://arxiv.org/abs/2504.06275 · HTML: https://mlsanity.com/paper/2504.06275/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 5 references that are in the map.
- First posted on arXiv 6 Mar 2025.

## Builds on (most cited first, 2 of 2)

- [A Hierarchical Neural Autoencoder for Paragraphs and Documents](https://mlsanity.com/paper/1506.01057/) (2015)
- [MemSum: Extractive Summarization of Long Documents Using Multi-Step Episodic Markov Decision Processes](https://mlsanity.com/paper/2107.08929/) (2021)

## Built on by (most cited first, 1 of 1)

- [A Hybrid Hierarchical 1D-CNN-BiLSTM Framework for Extractive Summarization of Biomedical and Clinical Text](https://mlsanity.com/paper/2609.13481/) (2026)

## Abstract (arXiv)

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.