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komats/mega-ssum

Mega-SSum A large-scale English sentence-wise speech summarization (Sen-SSum) dataset Consists of 3.8M+ synthesized speech, transcription, summary triplets Derived from the Gigaword dataset Rush+2015 Overview The dataset is divided into five splits: train/core/dev/eval/duc2003. (See below table) We added a new evaluation split "test" for in-domain evaluation. The train split is here: MegaSSum(train). orig. data split #samples #speakers total dur.… See the full description on the dataset page: https://huggingface.co/datasets/komats/mega-ssum.

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Dataset Card

Mega-SSum

—A large-scale English sentence-wise speech summarization (Sen-SSum) dataset
—Consists of 3.8M+ synthesized speech, transcription, summary triplets
—Derived from the Gigaword dataset Rush+2015

Overview

—The dataset is divided into five splits: train/core/dev/eval/duc2003. (See below table)
—We added a new evaluation split "test" for in-domain evaluation.
—The train split is here: MegaSSum(train).

orig. data	split	#samples	#speakers	total dur. (hrs)	ave. dur. (sec)	CR* (%)
Gigaword	train	3,800,000	2,559	11,678.2	11.1	26.2
Gigaword	core	50,000	2,559	154.6	11.1	25.8
Gigaword	valid	1,000	96	3.0	10.7	25.1
Gigaword	test	4,000	80	12.5	11.2	24.1
DUC2003	duc2003	624	80	2.1	12.2	27.5

CR (compression rate, %) = #words in summary / #words in transcription 100. Lower is shorter summary.

Notes

—The core set is identical to the first 50k samples of the train split.
—You may train your model and report the results only with the core set because the train split is very large.
—Using the entire train split is generally not recommended unless there are special reasons (e.g., to investigate the upper bound).
—The duc2003 split has four reference summaries for each speech. You can report the best score from 4 scores.
—Spoken sentences were generated using VITS Kim+2021 trained with LibriTTS-R Koizumi+2023.
—More details and some experiments on this dataset can be found here.

Citation

—This dataset Matsuura+2024:

  @inproceedings{matsuura24_interspeech,
    title     = {{Sentence-wise Speech Summarization}: Task, Datasets, and End-to-End Modeling with LM Knowledge Distillation},
    author    = {Kohei Matsuura and Takanori Ashihara and Takafumi Moriya and Masato Mimura and Takatomo Kano and Atsunori Ogawa and Marc Delcroix},
    year      = {2024},
    booktitle = {Interspeech 2024},
    pages     = {1945--1949},
  }

—The Gigaword dataset Rush+2015:

  @article{Rush_2015,
     title={A Neural Attention Model for Abstractive Sentence Summarization},
     journal={Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing},
     author={Rush, Alexander M. and Chopra, Sumit and Weston, Jason},
     year={2015}
  }

—VITS TTS Kim+2021:

  @InProceedings{pmlr-v139-kim21f,
    title = 	 {Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech},
    author =       {Kim, Jaehyeon and Kong, Jungil and Son, Juhee},
    booktitle = 	 {Proceedings of the 38th International Conference on Machine Learning},
    pages = 	 {5530--5540},
    year = 	 {2021},
  }

—LibriTTS-R Koizumi+2023:

  @inproceedings{koizumi23_interspeech,
    author={Yuma Koizumi and Heiga Zen and Shigeki Karita and Yifan Ding and Kohei Yatabe and Nobuyuki Morioka and Michiel Bacchiani and Yu Zhang and Wei Han and Ankur Bapna},
    title={{LibriTTS-R}: A Restored Multi-Speaker Text-to-Speech Corpus},
    year=2023,
    booktitle={Proc. INTERSPEECH 2023},
    pages={5496--5500},
  }