Automatic Text Summarization made simpler with Python

What is Text Summarization?

There is an enormous amount of textual information present in this world , and it is only growing every single day .

Think of the internet which  comprises news articles related to a wide range of topics webpages ,status updates, blogs and so much more. The data is unstructured and the best that we can do to navigate it is to use search and skim the results.

There is a great need to reduce much of this text data to shorter text while preserving the important  information contained in it. Summaries that capture the salient details, both so we can navigate it more effectively as well as check whether the larger documents contain the information that we are looking for.

Textual information in the form of digital documents quickly accumulates large amounts of data. Most of this huge volume of documents is unstructured and has not been organized into traditional databases. Processing documents is therefore a difficult task.

We cannot possibly create summaries of all of the text manually that’s where  automatic text summarization comes into light.

In their 2014 book on the subject titled “Automatic Text Summarization,” the authors provide 6 reasons why we need automatic text summarization tools.

  1. Summaries reduce reading time.

  2. When researching documents, summaries make the selection process easier.

  3. Automatic summarization improves the effectiveness of indexing.

  4. Automatic summarization algorithms are less biased than human summarizers.

  5. Personalized summaries are useful in question-answering systems as they provide personalized information.

  6. Using automatic or semi-automatic summarization systems enables commercial abstract services to increase the number of texts they are able to process.

Types of Text Summarization

As are no fixed guidelines for categorization on the techniques that we use for summary generation. Although for performing tasks in an organized way they are generally be divided into these following types:

  1. Short Tail Summarization: In this type of summary the input content is very short and precise. Even after having a short length it needs to contain important information about the text.

  2. Long Tail Summarization: As you might have already gasped by the name. The content here could be too long to be handled by a human being alone. It could contain text data from thousands of pages and books at once.

  3. Single Entity: When the input usually contains elements from just one source.

  4. Multiple Entities: In this the input contains elements from different document sources.

  5. General Purpose: In this type of Text Summarization Python has no attribute for the type of input is provided. The algorithm does not have a sense of the domain in which the text deals.

Approaches for automatic summarization

Summarization algorithms are either extractive or abstractive in nature based on the summary generated. Extractive algorithms form summaries by identifying and pasting together relevant sections of the text. Depending only on extraction of sentences from the original text. For such a reason, extractive methods yield naturally grammatical summaries and require relatively little linguistic analysis.

In contrast, abstractive algorithms are generally  most human-like which mimic the process of paraphrasing a text.In this approach it  may generate new text that is not present in the initial document. Texts summarized using this technique looks more human-like and produces condensed summaries which are easier to read . However, abstractive techniques are much harder to implement than extractive summarization techniques. Existing abstractive summarizers often rely on an extractive preprocessing component to produce the abstract of the text.

Applications of Text Summarization

  1. News: There are multiple applications of this technique in the field of News. It includes creating an introduction, Generating headlines, Embedding captions on pictures.

  2. Scientific Research: Algorithms are used to dig out important information from Scientific research papers. AI is outranking human beings in doing so.

  3. Social Media Posting: Content on Social media is preferred to be concise. Companies use this technique to convert long blog articles into shorter ones suited for the audience.

  4. Creating Study Notes: Many applications use this process to create student notes from vast syllabus and content.

  5. Conversation Summary: Long conversations and meeting recording could be first converted into text and then important information could be fetched out of them.

  6. Movie Plots and Reviews: The whole movie plot could be converted into bullet points through this process.

Automatic Text Summarization libraries in Python 





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