Do Away With Manual Work via Text Summarization Tool

Dedicating time sincerely for reading articles till the end is no more a feasible option, considering the scarcity of time. Text summarization tool automatically shortens longer texts and generates summaries to pass the intended message & help enterprises produce content efficiently


  • The text summarization tool reduces the efforts of users put in researching the relevant information - thus decreasing their reading time as well
  • Saves time and effort of content editors and authors which otherwise is invested in creating summaries of articles manually
  • Uses sequence to sequence model for abstractive summarization method andBERT model for extractive summarization

The Challenge

The International Data Corporation has predicted that the total amount of digital data circulating across the world would shoot up from 4.4 ZT in 2013 to 180 ZT in 2025.

Given this, going in the quest for absorbing relevant data amidst a generous amount of information available today can be an overwhelming task for anyone! Further, content authors and editors are forced to spend half of their time in writing summaries for the articles, impacting their productivity profoundly.

Besides, users also prefer to get the essence of the story within a few seconds or minutes instead of devoting prolonged periods.

The Solution

Srijan has built a tool, namely, text summarization, to automate the manual process of creating summaries for blogs, articles, news, etc by extracting the key information and compressing it into a shorter version - ensuring that actual meaning remains intact!

The tool comprises of two divergent approaches for summarization-

  1. Summarization based on abstraction
  2. Summarization based on extraction

The abstraction method involves paraphrasing and shortening sentences/parts of the source document. It can also create new phrases and sentences that portray the most useful information from the original text - similar to what humans do.

one text box in left with result in rightContrary to it, the extraction method works by analyzing the importance of each sentence in a given document. Typically, sentences are labeled in terms of their importance in the document. A summary is thus generated by picking several top-scoring sentences.

This method allows you to put the limit to the number of sentences in which you want a summary. Besides, you can also consolidate 4-5 articles at a time and generate a summary out of it.

Two boxes in left and one in right with content in itThe abstraction method works best over time as neural networks keep on learning through data while the extraction method is quite easier to use and popular as well.

The technology stack used for it-

  1. Sequence to sequence model
  2. Deep learning neural network
  3. BERT model

How Does the Solution Work? 

Watch this video to understand its working-

Key Takeaway

Producing intelligent text summaries will help editors and content authors do away with its manual creation, thereby saving their time and effort for new content ideas.

Further, it would be a breeze for consumers to find, read and absorb relevant data within seconds, apart from an effective arrangement of content.


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