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Weakly Supervised Extractive Summarization with Attention
Automatic summarization aims to extract important information from large amounts of textual data in order to create a shorter version of the original texts while preserving its information.
Automating Template Creation for Ranking-Based Dialogue Models
We compare this method to a random baseline that randomly assigns templates to clusters as well as a strong baseline that performs the sentence encoding and the utterance clustering sequentially.
A testing based extraction algorithm for identifying significant communities in networks
In addition, we carry out a simulation study to assess the effectiveness of ESSC in networks with various types of community structure, including networks with overlapping communities and those with background vertices.
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