Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Distributed representation learned with neural networks has recently shown to be effective in modeling natural languages at fine granularities such as words, phrases, and even sentences. Whether and how such an approach can be extended to help model larger spans of text, e.g., documents, is intriguing, and further investigation would still be desirable.  aims to enhance neural network models for such a purpose. A typical problem of document-level modeling is automatic summarization, which aims to model documents in order to generate summaries. ,  propose neural models to train computers not just to pay attention to specific regions and content of input documents with attention models, but also distract them to traverse beten different content of a document so as to better grasp the overall meaning for summarization. Without engineering any features,  train the models on two large datasets. The models achieve the state-of-the-art performance, and they significantly benefit from the distraction modeling, particularly when input documents are long.
['Qian Chen', 'Xiaodan Zhu', 'Zhen-Hua Ling', 'Si Wei', 'Hui Jiang']
I am writing a research paper on a idea Distraction-Based Neural Networks for Document Summarization
[{"from": "human", "value": "I am writing a research paper on a idea Distraction-Based Neural Networks for Document Summarization"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Distributed representation learned with neural networks has recently shown to be effective in modeling natural languages at fine granularities such as words, phrases, and even sentences. Whether and how such an approach can be extended to help model larger spans of text, e.g., documents, is intriguing, and further investigation would still be desirable.  aims to enhance neural network models for such a purpose. A typical problem of document-level modeling is automatic summarization, which aims to model documents in order to generate summaries. ,  propose neural models to train computers not just to pay attention to specific regions and content of input documents with attention models, but also distract them to traverse beten different content of a document so as to better grasp the overall meaning for summarization. Without engineering any features,  train the models on two large datasets. The models achieve the state-of-the-art performance, and they significantly benefit from the distraction modeling, particularly when input documents are long."}]