PubMed Article Summarization Dataset

PubMed Summarization Dataset

By ccdv (From Huggingface) [source]

About this dataset

> > The dataset consists of multiple files, including validation.csv, train.csv, and test.csv. Each file contains a combination of articles and their respective abstracts. The articles are sourced directly from PubMed, ensuring they represent a wide range of topics across various scientific disciplines. > > In order to provide reliable datasets for different purposes, the files have been carefully curated to serve specific functions. validation.csv contains a subset of articles with their corresponding abstracts that can be used for validating the performance of summarization models during development. train.csv features a larger set of article-abstract pairs specifically intended for training such models. > > Finally, test.csv serves as an independent evaluation set that allows developers to measure the effectiveness and generalizability of their summarization models against unseen data points. By using this test set, researchers can assess how well their algorithms perform in generating concise summaries that accurately capture the main findings and conclusions within scientific articles. > > Researchers in natural language processing (NLP), machine learning (ML), or any related field can utilize this dataset to advance automatic text summarization techniques focused on scientific literature. Whether it's building extractive or abstractive methods or exploring novel approaches like neural networks or transformer-based architectures, this rich dataset provides ample opportunities for experimentation and progress in the field. >

How to use the dataset

> > Introduction: > > Dataset Structure: > > - article: The full text of a scientific article from the PubMed database (Text). > - abstract: A summary of the main findings and conclusions of the article (Text). > > Using the Dataset: > To maximize the utility of this dataset, it is important to understand its purpose and how it can be utilized: > > - Training Models: The train.csv file contains articles and their corresponding abstracts that can be used for training summarization models or developing algorithms that generate concise summaries automatically. > > - Validation Purposes: The validation.csv file serves as a test set for fine-tuning your models or comparing different approaches during development. > > - Evaluating Model Performance: The test.csv file offers a separate set of articles along with their corresponding abstracts specifically designed for evaluating the performance of various summarization models. > > Tips for Utilizing the Dataset Effectively: > > - Preprocessing: Before using this dataset, consider preprocessing steps such as removing irrelevant sections (e.g., acknowledgments, references), cleaning up invalid characters or formatting issues if any exist. > > - Feature Engineering: Explore additional features like article length, sentence structure complexity, or domain-specific details that may assist in improving summarization model performance. > > - Model Selection & Evaluation: Experiment with different summarization algorithms, ranging from traditional extractive approaches to more advanced abstractive methods. Evaluate model performance using established metrics such as ROUGE (Recall-Oriented Understudy for Gisting Evaluation). > > - Data Augmentation: Depending on the size of your dataset, you may consider augmenting it further by applying techniques like data synthesis or employing external resources (e.g., pre-trained language models) to enhance model performance. > > Conclusion:

Research Ideas

> - Textual analysis and information retrieval: Researchers can use this dataset to analyze patterns in scientific literature or conduct information retrieval tasks. By examining the relationship between article content and its abstract, researchers can gain insights into how different sections of a scientific paper contribute to its overall summary.

Acknowledgements

> If you use this dataset in your research, please credit the original authors. > Data Source > >

License

> > > License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication > No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: validation.csv

File: train.csv

File: test.csv

Acknowledgements

> If you use this dataset in your research, please credit the original authors. > If you use this dataset in your research, please credit ccdv (From Huggingface).