Datasets:

Modalities:
Text
Formats:
csv
Languages:
Urdu
DOI:
Libraries:
Datasets
pandas
License:
File size: 2,030 Bytes
2ef1160
 
4cb753c
 
 
 
 
 
 
 
 
 
2ef1160
4cb753c
 
 
 
 
 
ecc64eb
15ba52a
4cb753c
 
 
d714a5e
 
4cb753c
 
d714a5e
 
e9d479c
d714a5e
 
4cb753c
 
 
 
 
 
 
3afd100
 
 
 
 
 
4cb753c
 
 
9091e71
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
---
license: cc-by-4.0
task_categories:
- summarization
language:
- ur
tags:
- Urdu
- Summarization
pretty_name: Urdu Summarization (BBC and DW Urdu News)
size_categories:
- 10K<n<100K
---
# Urdu_DW-BBC-512

## Dataset Description

- **Homepage:** 
- **Repository:** 
- **Paper: https://dl.acm.org/doi/10.1145/3675780** 
- **Point of Contact: [email protected]** 

### Dataset Summary

- Urdu Summarization Dataset containining 76,637 records of Article + Summary pairs scrapped from BBC Urdu and DW Urdu News Websites.
- Preprocessed Version: upto 512 tokens (~words); removed URLs, Pic Captions etc

### Supported Tasks and Leaderboards
Summarization: Extractive and Abstractive
- urT5 adapted from mT5 having monolingual vocabulary only; 40k tokens of Urdu.
  - Fine-tuned version @ https://huggingface.co/mbshr/urt5-base-finetuned, ref to https://dl.acm.org/doi/10.1145/3675780 for details.
- ROUGE-1 F Score: 40.03 combined, 46.35 BBC Urdu datapoints only and 36.91 DW Urdu datapoints only)
- BERTScore: 75.1 combined, 77.0 BBC Urdu datapoints only and 74.16 DW Urdu datapoints only

### Languages

Urdu.

### Data Fields

    - url: URL of the article from where it was scrapped (BBC Urdu URLs in english topic text with number & DW Urdu with Urdu topic text)
      dtype: {string}
    - Summary: Short Summary of article written by author of article like highlights.
      dtype: {string}
    - Text: Complete Text of article which are intelligently trucated to 512 tokens.
      dtype: {string}

### Citation Information

https://dl.acm.org/doi/10.1145/3675780

*Bibtex*
@article{10.1145/3675780,
author = {Munaf, Mubashir and Afzal, Hammad and Mahmood, Khawir and Iltaf, Naima},
title = {Low Resource Summarization using Pre-trained Language Models},
year = {2024},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
issn = {2375-4699},
url = {https://doi.org/10.1145/3675780},
doi = {10.1145/3675780},
journal = {ACM Trans. Asian Low-Resour. Lang. Inf. Process.},
month = {jul},
}