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Abstractive summarization from Audio Transcription

arXiv 2408.04639 ↗First posted 30 Jul 2024cs.CL cs.IR cs.LGcited by 0

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Abstract (arXiv)

Currently, large language models are gaining popularity, their achievements are used in many areas, ranging from text translation to generating answers to queries. However, the main problem with these new machine learning algorithms is that training such models requires large computing resources that only large IT companies have. To avoid this problem, a number of methods (LoRA, quantization) have been proposed so that existing models can be effectively fine-tuned for specific tasks. In this paper, we propose an E2E (end to end) audio summarization model using these techniques. In addition, this paper examines the effectiveness of these approaches to the problem under consideration and draws conclusions about the applicability of these methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 39 references that are in the map. First posted on arXiv 30 Jul 2024.

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15 of 39 references.

References · 15

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  3. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  4. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  5. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 4,057
  6. 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,418
  7. 2016WaveNet: A Generative Model for Raw Audiocited by 1,722
  8. 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,102
  9. 2024DoRA: Weight-Decomposed Low-Rank Adaptationcited by 758
  10. 2023The Flan Collection: Designing Data and Methods for Effective Instruction Tuningcited by 739
  11. 2023AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuningcited by 357
  12. 2023A Survey on Model Compression for Large Language Modelscited by 269
3 more
  1. 2018WikiHow: A Large Scale Text Summarization Datasetcited by 204
  2. 2023LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Modelscited by 196
  3. 2018How2: A Large-scale Dataset for Multimodal Language Understandingcited by 138

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  1. 2024Prompting Large Language Models with Audio for General-Purpose Speech Summarization
  2. 2024Sentence-wise Speech Summarization: Task, Datasets, and End-to-End Modeling with LM Knowledge Distillation
  3. 2024TriSum: Learning Summarization Ability from Large Language Models with Structured Rationale
  4. 2021StreamHover: Livestream Transcript Summarization and Annotation
  5. 2025Advancing Speech Summarization in Multi-modal LLMs with Reinforcement Learning
  6. 2024Think Carefully and Check Again! Meta-Generation Unlocking LLMs for Low-Resource Cross-Lingual Summarization
  7. 2025Towards Audio Token Compression in Large Audio Language Models
  8. 2023Retrieval-Augmented Text-to-Audio Generation