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Hierarchical Attention Model for Improved Machine Comprehension of Spoken Content
[article]
2017
arXiv
pre-print
Multimedia or spoken content presents more attractive information than plain text content, but the former is more difficult to display on a screen and be selected by a user. As a result, accessing large collections of the former is much more difficult and time-consuming than the latter for humans. It's therefore highly attractive to develop machines which can automatically understand spoken content and summarize the key information for humans to browse over. In this endeavor, a new task of
arXiv:1608.07775v3
fatcat:dz3zykliy5hfzdwdtgkopvqmue