Abstractive: Generate new sentences using natural language generation techniques that distil out the essence of the original text, just like a human would.Extractive: Puts together important sentences, extracted from the original text doc, to create a summary.Text summarization can be broadly classified into two categories: abstractive and extractive summarization. Text summarization is a Natural Language Processing task with several applications when building conversational apps or working with transcripts. Text Summarization from Conversation Transcripts We shall study various Text Summarization techniques that we can apply once we obtain a transcript or even a text document in this article. Abstractive and extractive summaries are produced to make the conversations easier to read.Īutomatic speech recognition (ASR) enables the comprehension of natural language and is used in the process of recording, analyzing, and translating human speech into text (STT). Here, an AI generated text summary can be useful, to help readers quickly understand the salient points of the conversation. Reading the whole document can be painstaking as it often contains a lot of sections like niceties, chatter etc which may not be part of the core objectives. It communicates the conversation's main points without the need to exhaustively read through an entire transcript. For Speech to Text (STT) practitioners, especially those working on conversational AI applications, the ability to create transcript summaries is crucial.
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