JRC1995/Abstractive-Summarization

Implementation of abstractive summarization using LSTM in the encoder-decoder architecture with local attention.

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Emerging

This project helps anyone who needs to quickly get the main points from long documents, like customer reviews or articles. It takes in a piece of text and outputs a shorter, human-like summary that captures the essential information, rather than just pulling sentences verbatim. This is ideal for researchers, analysts, or content creators needing to digest information efficiently.

168 stars. No commits in the last 6 months.

Use this if you need to create concise, original summaries from lengthy text inputs, such as product reviews or news articles, saving time on manual reading.

Not ideal if you need an exact, verbatim extraction of key sentences, or if your text inputs are extremely short and don't require summarization.

document-analysis content-briefing text-understanding information-digest
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 10 / 25
Maturity 16 / 25
Community 22 / 25

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Stars

168

Forks

59

Language

Jupyter Notebook

License

MIT

Last pushed

Dec 30, 2019

Commits (30d)

0

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