Summary Generator


A text summarizer is a computer program that generates short text  summaries  of longer text documents. Text summarizers are commonly used in news , e-mail clients , blogs text messaging or as an aid for readers who wish to survey large amounts of text. Today many text summary generation techniques exist. Most of them rely on statistical methods which show state-of-the-art results in terms of both subjective quality and speed.

Whether you're a student researching for an essay, or just someone who wants to save time when reading online content, Text Summarizer is the tool for you. Simply type in a long text and our advanced summarizer engine does all the work! We have also made sure that your privacy is protected - we do not store any of your data.


Text lengths that can be summarized range from 200 to 400 words. All a person has to do is get the URL for the article or book text and then copy-paste it into the input field.

No more wasting hours of your time trying to read something you don't want to read, now there's an app for that! Text summarizer churns out fancy summaries while giving authors some space by not including any quotes or thoughts (just like in Hollywood). Paragraphs are compressed down into cut and dry bullets so readers will know all about what happened without having to tediously pore through all those extra details with an internet connection.. 

Fixing your reading slump is as simple as clicking submit and congratulations! You've summary of a long text!


What is a text summarizer?

Text summarizer is a tool that helps to generate summary of long text.  It can be used to quickly get an insight of text.  Most text summarizing algorithms reduce text size by reporting some key information and a conversion the majority of text into questions, so it is easier to read for a human being.

Text summarizer uses following steps:

1- Read text file and store it in a data structure called paragraph list (PL). PL is ordered sequence of paragraphs which are stored as linked lists that linked together by previous/next pointer flag. Each element of PL has reference to beginning and end position of the text sentence.  

2- The second step is preprocessing text, where we remove stop words (such as "a" or "the" ) from text sentences and each sentences transformed to a list of text lines. Lines are stored in a text line data structure that contains text sentence (the text itself) and its length.

3- Summarize text sentences by using some summarization techniques such as:

3.1- Collocation based summarizer is applied for each text line. In this part, the summarizer extracts an array of collocation words which have higher frequency than any other words in the text to the summary sentences device (STS). The STS has one output port to store all summary sentences that will be sent to next step using coupled queue devices linked together by FIFO channel connection. The coupling between these two devices establishes a single path flow control from source text file to STS, then to summary text file.

3.2- Semantic based summarizer is applied for each text line. In this part, the summarizer matches text lines of source text with lexical knowledge by using a lexical knowledge base created from text corpora and text dictionaries . The matched words are sorted into semantic hierarchy based on their similarities' degree. Then the linked words in semantic tree structure are merged to create summary sentences to STS which will be transferred via coupled queue devices with FIFO channel connection as mentioned before.

3.3- Phrase based summarizer is applied for each text line. In this part, the summarizer extracts phrases that have higher occurrence than other phrases from text corpus, then merges them together to form higher level text units. Finally, a text summarizer generates text summary as summarized from the higher level text unit.

In order to produce high-quality summaries, it is common for text summarizers to be trained with many text documents, even whole text corpora. However, this requires large amounts of human effort and time and thus is not suitable for automatic summarization applications such as newswire services or automatically produced summaries in digital libraries.

Therefore, most current research on text summarizers instead relies on using some kind of statistical model that captures how words tend to co-occur in text. It has been shown that these models achieve state-of-the-art results both in terms of subjective quality (Brysbaert, De Baets, & New, 2004 ; Koehn et al., 2006 ) and when evaluated automatically against text lengths (Brysbaert, Spyns, Ramonas, & Scerri , 2008 ).

 


Paraphrasing vs summarizing

It’s good idea to paraphrase texts when you plan on using them as knowledge sources for further writing assignments — this will prevent possible accusations of plagiarism; it’s also useful for students who don’t feel confident about their style of writing. However, one should bear in mind that paraphrasing text is not the same as summarizing text.

Summarizing text means that you use your own words to produce a text overview (text with less details and more general information) of another text — this task implies that there should be no text copying involved. The main idea of summarization is condensing longer text into shorter overview text — it’s a good practice if you are running out of time or space, trying to explain an important/intricate topic quickly in your work.

However, remember that if you decide to use others’ texts for creating summaries of them (or paraphrases), always make sure that the sources are properly acknowledged by using quotation marks or some other form of citation.


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