Get To The Point: Summarization with Pointer-Generator Networks
Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text). However, these models have two shortcomings: they are liable to reproduce factual details inaccurately, and they tend to repeat themselves. In this work we propose a novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways. First, we use a hybrid pointer-generator network that can copy words from the source text via pointing, which aids accurate reproduction of information, while retaining the ability to produce novel words through the generator. Second, we use coverage to keep track of what has been summarized, which discourages repetition. We apply our model to the CNN / Daily Mail summarization task, outperforming the current abstractive state-of-the-art by at least 2 ROUGE points.
Project Overview Marking the keywords without knowing the dictionary is a big issue and that too with relative data. Here the data will be having offline dictionary for proper marking with summarization. So here the data marking is done with respect to offline dictionary and once the same data sent to server where the machine learning happening and point summarization updates and will be changed according to the future dictionary.
Processor - Dual Core
Speed - 1.1 G Hz
RAM - 512 MB (min)
Hard - 20 GB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse
Operating System : Windows xp,7,8
Front End : Java 7
Technology : Swings,Core java
IDE : Netbeans.
Below code is for highlighting the content in the document.
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