Dialogue Summarization: A Deep Learning Approach
This article was published as a part of the Data Science Blogathon.
Dialogue Summarization: Its types and methodology
Image cc: Aseem Srivastava
Summarizing long pieces of text is a challenging problem. Summarization is done primarily in two ways: extractive approach and abstractive approach. In this work, we break down the problem of meeting summarization into extractive and abstractive components which further collectively generate a summary of the conversation.
What is Dialogue Summarization?
Humans are social animals, we exchange ideas, share information, and make plans with each other. Text and Speech are the two common conversation mediums, but mostly it’s speech. With the abundance of digital conversation happening over online messaging, IRC, meeting platforms, and the ubiquity of automatic speech recognition systems come vast amounts of meeting transcripts.
Therefore, the need to succinctly summarize the content of the conversation naturally arises. Several methods of generating summaries have been proposed. A very standard and crucial application