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license: cc-by-4.0
dataset_info:
features:
- name: id
dtype: string
- name: audio
dtype: audio
- name: transcription
dtype: string
- name: summary
dtype: string
- name: summary1
dtype: string
- name: summary2
dtype: string
- name: summary3
dtype: string
splits:
- name: core
num_bytes: 17683719490.0
num_examples: 50000
- name: duc2003
num_bytes: 244384744.0
num_examples: 624
- name: validation
num_bytes: 342668783.0
num_examples: 1000
- name: test
num_bytes: 1411039659.0
num_examples: 4000
download_size: 19837902893
dataset_size: 19681812676.0
configs:
- config_name: default
data_files:
- split: core
path: data/core-*
- split: duc2003
path: data/duc2003-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
# 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](https://aclanthology.org/D15-1044/)
# 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)](https://huggingface.co/datasets/komats/mega-ssum-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](https://proceedings.mlr.press/v139/kim21f.html) trained with LibriTTS-R [Koizumi+2023](https://www.isca-archive.org/interspeech_2023/koizumi23_interspeech.html).
- More details and some experiments on this dataset can be found [here](https://www.isca-archive.org/interspeech_2024/matsuura24_interspeech.html#).
# Citation
- This dataset [Matsuura+2024](https://www.isca-archive.org/interspeech_2024/matsuura24_interspeech.html):
```
@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](https://aclanthology.org/D15-1044/):
```
@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](https://proceedings.mlr.press/v139/kim21f.html):
```
@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](https://www.isca-archive.org/interspeech_2023/koizumi23_interspeech.html):
```
@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},
}
```
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