The internet world is full of blogs, articles, web pages, status updates and so on. It can be quite overwhelming to look for important data when there are so many sources to read from. This is where auto text summarization comes to the rescue. Auto text summarization is the solution to the ever-growing text data available on the net today.
What is Automatic Text Summarization?
Auto text summarization scans through large documents and creates a short summary out of them by determining the most important sentences and the keywords that are being repeated in the source document. It makes use of an algorithm that extracts these significant sentences from a document and returns them in a format which is easy to read and in a sequential order presenting the gist of what the topic intends to explain.
This tool first calculates the word count of a document then shortlists the first 100 most common words which are then sorted and stored. The more keywords a sentence uses, the more score it gets and higher the ranking. At the end, the top sentences with high scores are extracted to make the summary based on the original text of the document. Thus auto text summarization is the process of creating a short or brief version of a longer document.
Textual information in the form of digital documents quickly accumulates to huge amounts of data. Most of this large volume of documents is unstructured: it is unrestricted and has not been organized into traditional databases. Processing documents is therefore a perfunctory task, mostly due to the lack of standards. — Page xix, Automatic Text Summarization, 2014.
Why Do We Need Auto Text Summarization?
If you find yourself skim reading articles on the net or using search or find tools to get the maximum information in the least amount of time, you have answered this question yourself. Auto text summarization is the most effective way to break down longer walls of text into crisp bite-sized texts that are easier to follow and retain. You can quickly access it, and if you find it useful you can spend more time going into the details, otherwise just skip it and jump on the next article, thereby saving considerable amount of time reading through unnecessary detailed text documents. Professionals like lawyers, paralegals, and other researchers especially find this very useful as they have to go through hundreds of documents and files every day. So instead of spending too many hours leafing through documents, they can quickly access documents using the text summarizer.
Some other benefits include:
- Summarized data makes the selection process easier.
- It improves the effectiveness of indexing.
- The algorithms used by the auto text summarizers are less biased when compared to humans.
- Personalized summaries can be very helpful in providing personalized information.
- The number of documents that you can read increases tremendously once the length of the texts are reduced.
Examples of Auto Text Summarization
We see many examples of auto text summarization in our day to day lives that we never realized before. Some examples include the headlines of an article in the paper, outlines or notes for students, minutes of a meeting, or chronology of an event. All these examples essentially involve the process of presenting a textual summary of a long document.
Other Auto Text Summarization Applications
- Entity Timelines: Auto text summarizer can immediately provide concise information about the happenings around an entity within a particular time period.
- Storyline of Events: It provides the background information for an event under consideration. Additionally, it also provides structured timelines for an entity.
- Sentence Compression: News headlines are a perfect example of this application. They present the entire idea of the document compressed in a single sentence.
Methods of Auto Text Summarization
The different dimensions of text summarization can be generally categorized based on its input type (single or multi document), purpose (generic, domain specific, or query-based) and output type (extractive or abstractive). — A Review on Automatic Text Summarization Approaches, 2016.
There are two main methods to summarize documents. They are:
- Extractive Method: This method makes use of sentences and phrases from the original text document to create a short summary by using the summarizer. According to the importance and relevance of the sentences and the keywords they hold, the most relevant sentences are extracted from the source document.
- Abstractive Method: This method does not use any sentences or phrases from the original source document to make the summary version but instead generates entirely new ones that reflect the essence of what the documents intends to say. This is a more difficult approach as compared to extractive method but it is ultimately used by humans in co-operation with machines. The content is selected and compressed from the source document so it can be used to generate new sentence and phrases.