الفهرس 
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Abstract
Twitter, an online micro blog that enables its users to send and read text-based
posts known as ”tweets”, became one of the most commonly used social
networks. However, it is not available to find a summary of tweets about a certain
Arabic hot topic. This thesis presents an algorithm for summarizing Arabic micro
blogging posts. Algorithm processes collections of tweets on specific topics and
creates short summaries from these collections. The goal is to produce
summaries that are similar to what a human would produce for the same
collection of tweets on a specific topic. The problem is formulated as a regression
problem, not a binary classification based problem. Instead of classifying the
tweets to be important and not important, each tweet is given a score that
determines to which extent this tweet is candidate to be in the summary. This
helps generate the summary according to the user predefined compression rate.
The proposed system is evaluated by using two methods namely, the
Precision and Recall method and the Normalized Discounted Cumulative Gain
(NDCG). The results of summarization of the proposed system are compared
with that obtained by the well-known multi-document summarization algorithms
including; SumBasic, TF-IDF, PageRank, and MEAD summarizer. Also, three
volunteers are asked to make summarization of several groups of tweets and the
results obtained by the proposed system are compared with that obtained by the
manual summarization experiments.
Demonstrated two ways were used in the evaluation Precision/Recall and
NDCG indicate that our proposed system produced good level of summarization
performance compared to the other systems and compared to the manual
summarization. the proposed strategy provides best results of 50% compared to
the manual summarization of 53% using Precision / Recall method and 91%
compared to the manual summarization of %95 using NDCG method.