nyamuda/extractive-summarization

Model Description
This model is a fine-tuned version of t5-small, fine tuned for summarizing scientific and medical texts using the PubMed dataset. This allows the model to accurately condense lengthy research articles, papers, and technical abstracts into concise summaries, making it a valuable tool for students, researchers, and professionals in the scientific and medical fields.

Summarization Type
The model uses extractive summarization, which means it picks out key sentences from the original text to create clear, concise summaries that keep the main information. Instead of creating new phrases, it focuses on keeping the important ideas and language of the scientific content intact, providing accurate and reliable summaries.

Intended Use Cases
This model is perfect for summarizing research papers, scientific articles, and educational materials. It’s especially useful for helping users quickly understand the main points of long scientific texts, making it a great choice for educational and research applications.

Training Details
The model was fine-tuned using the PubMed dataset, which includes high-quality summaries of biomedical research articles.

Deployment
Users can use this model in different ways, like summarizing documents, analyzing scientific content, and supporting educational platforms that simplify complex scientific materials.