Text Summarization using Deep Learning

Doing cool things with data!

Priya Dwivedi
Towards Data Science

Text Summarization

Introduction

With the rise of internet, we now have information readily available to us. We are bombarded with it literally from many sources — news, social media, office emails to name a few. If only someone could summarize the most important information for us! Deep Learning is getting there. Through the latest advances in sequence to sequence models, we can now develop good text summarization models.

Text Summarization can be of two types:

1. Extractive Summarization — This approach selects passages from the source text and then arranges it to form a summary. One way of thinking about this is like a highlighter underlining the important sections. The main idea is that the summarized text is a sub portion of the source text.

2. Abstractive Summarization -In contrast, abstractive approach involves understanding the intent and writes the summary in your own words. I think of this as analogous to a pen.

Naturally abstractive summarization is the more challenging problem here. This is one domain where machine learning has made slow progress. It is a difficult problem since creating abstractive summaries requires good command of the subject and of natural language which can both be difficult tasks…

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