As an example, here is the Google article resumed by SUMMRY.
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Research Blog: Text summarization with TensorFlow Being able to develop Machine Learning models that can automatically deliver accurate summaries of longer text can be useful for digesting such large amounts of information in a compressed form, and is a long-term goal of the Google Brain team.
One approach to summarization is to extract parts of the document that are deemed interesting by some metric and join them to form a summary.
Above we extract the words bolded in the original text and concatenate them to form a summary.
It turns out for shorter texts, summarization can be learned end-to-end with a deep learning technique called sequence-to-sequence learning, similar to what makes Smart Reply for Inbox possible.
In this case, the model reads the article text and writes a suitable headline.
In those tasks training from scratch with this model architecture does not do as well as some other techniques we're researching, but it serves as a baseline.
We hope this release can also serve as a baseline for others in their summarization research.