End-to-End Speech Summarization Using Restricted Self-Attention

Author:

Sharma Roshan1,Palaskar Shruti1,Black Alan W1,Metze Florian1

Affiliation:

1. Carnegie Mellon University,Pittsburgh,PA,USA

Publisher

IEEE

Reference30 articles.

1. Reformer: The efficient transformer;kitaev,0

2. ETC: Encoding Long and Structured Inputs in Transformers

3. Linformer: Self-attention with linear complexity;wang,2020

4. Big bird: Transformers for longer sequences;zaheer;Advances in neural information processing systems,2020

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1. AugSumm: Towards Generalizable Speech Summarization Using Synthetic Labels from Large Language Models;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14

2. Train Long and Test Long:Leveraging Full Document Contexts in Speech Processing;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14

3. ED-CEC: Improving Rare word Recognition Using ASR Postprocessing Based on Error Detection and Context-Aware Error Correction;2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU);2023-12-16

4. Espnet-Summ: Introducing a Novel Large Dataset, Toolkit, and a Cross-Corpora Evaluation of Speech Summarization Systems;2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU);2023-12-16

5. Summarize While Translating: Universal Model With Parallel Decoding for Summarization and Translation;2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU);2023-12-16

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