Summary of Abstractive Summarization From Audio Transcription, by Ilia Derkach
Abstractive summarization from Audio Transcription
by Ilia Derkach
First submitted to arxiv on: 30 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Information Retrieval (cs.IR); Machine Learning (cs.LG); Audio and Speech Processing (eess.AS)
GrooveSquid.com Paper Summaries
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper aims to develop an end-to-end (E2E) audio summarization model utilizing large language models that can be fine-tuned for specific tasks using existing models. The authors leverage techniques like LoRA and quantization to overcome the computational requirements of training such models, making them more accessible to smaller organizations. By exploring the effectiveness of these methods in the context of audio summarization, this research aims to contribute to the development of efficient and adaptable AI solutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to make big language models work better for specific tasks without needing super powerful computers. The researchers show how to fine-tune existing models using special techniques like LoRA and quantization. This can help make AI more accessible to people who don’t have huge computing resources. The study looks at how well these methods work in the context of summarizing audio recordings. |
Keywords
» Artificial intelligence » Lora » Quantization » Summarization
