| --- |
| 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}, |
| } |
| ``` |
|
|
