Arabic Auto Summarize:

Determines key points by analyzing Arabic document and assigning a score to each sentence. Sentences that contain words used frequently in the document are given a higher score. You can then choose a percentage of the highest-scoring sentences to display in the summary. It works best on well-structured documents such as reports, articles, and scientific papers.

It cuts wordy copy to the bone by counting words and ranking sentences. First, it identifies the most common words in the document and assigns a "score" to each word, the more frequently a word is used, the higher the score. Then, it "averages" each sentence by adding the scores of its words and dividing the sum by the number of words in the sentence, the higher the average, the higher the rank of the sentence. It can summarize texts to specific number of sentences or percentage of the original copy.

We use statistical approach, with some attention apparently paid to:

• Location: leading sentences of paragraph, title, introduction, and conclusion.
• Fixed phrases: in-text summaries.
• Frequencies of words, phrases, proper names.
• Contextual material: query, title, headline, initial paragraph.

The motivation for this class is the range of applications for key phrases:

• Mini-summary: Automatic key phrase extraction can provide a quick mini-summary for a long document. For example, it could be a feature in a web sites; just click the summarize button when browsing a long web page.
• Highlights: It can highlight key phrases in a long document, to facilitate skimming the document.
• Author Assistance: Automatic key phrase extraction can help an author or editor who wants to supply a list of key phrases for a document. For example, the administrator of a web site might want to have a key phrase list at the top of each web page. The automatically extracted phrases can be a starting point for further manual refinement by the author or editor.
• Text Compression: On a device with limited display capacity or limited bandwidth, key phrases can be a substitute for the full text. For example, a web page could be reduced for display on a Twitter post.

Example Output 1: