Quite a considerable effort. Depends on the quality and consistency of the results. Also the domain of language being summarized. Similarities to language translation semantics. Note too the large scale text datasets being summarized. See also, The Google Brain Team.
Text summarization with TensorFlow
Wednesday, August 24, 2016
Posted by Peter Liu, Software Engineer, Google Brain Team
Every day, people rely on a wide variety of sources to stay informed -- from news stories to social media posts to search results. 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.
Summarization can also serve as an interesting reading comprehension test for machines. To summarize well, machine learning models need to be able to comprehend documents and distill the important information, tasks which are highly challenging for computers, especially as the length of a document increases.
In an effort to push this research forward, we’re open-sourcing TensorFlow model code for the task of generating news headlines on Annotated English Gigaword, a dataset often used in summarization research. We also specify the hyper-parameters in the documentation that achieve better than published state-of-the-art on the most commonly used metric as of the time of writing. Below we also provide samples generated by the model. .... "
Subscribe to:
Post Comments (Atom)
No comments:
Post a Comment