# Dataset Card for Dataset Name

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Inshorts News dataset Inshorts provides a news summary in 60 words or less. Inshorts is a news service that offers short summaries of news from around the web. This dataset contains headlines and a summary of news items and their source.

## Dataset Details

### Dataset Description

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### Dataset Sources [optional]

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- **Paper [optional]:** An abstractive text summarization technique using transformer model with self-attention mechanism
- https://paperswithcode.com/paper/an-abstractive-text-summarization-technique

Neural Computing and Applications 2023
Sandeep Kumar, Arun Solanki

Creating a summarized version of a text document that still conveys precise meaning is an incredibly complex endeavor in natural language processing (NLP). Abstract text summarization (ATS) is the process of using facts from source sentences and merging them into concise representations while maintaining the content and intent of the text. Manually summarizing large amounts of text are challenging and time-consuming for humans. Therefore, text summarization has become an exciting research focus in NLP. This research paper proposed an ATS model using a Transformer Technique with Self-Attention Mechanism (T2SAM). The self-attention mechanism is added to the transformer to solve the problem of coreference in text. This makes the system to understand the text better. The proposed T2SAM model improves the performance of text summarization. It is trained on the Inshorts News dataset combined with the DUC-2004 shared tasks dataset. The performance of the proposed model has been evaluated using the ROUGE metrics, and it has been shown to outperform the existing state-of-the-art baseline models. The proposed model gives the training loss minimum to 1.8220 from 10.3058 (at the starting point) up to 30 epochs, and it achieved model accuracy 48.50% F1-Score on both the Inshorts and DUC-2004 news datasets.
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## Uses

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## Dataset Structure

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## Dataset Creation

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### Source Data

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Kaggle and Inshort news app

#### Data Collection and Processing

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web scrapping 
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### Annotations [optional]

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#### Personal and Sensitive Information

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## Bias, Risks, and Limitations

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### Recommendations

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Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

## Citation [optional]

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https://doi.org/10.1007/s00521-023-08687-7
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## Glossary [optional]

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