Proceedings ArticleDOI
Text Summarization for Social Network Conversation
Ahmed T. Sadiq,Yossra H. Ali,M.Sc. Mohammad Natiq Fadhil +2 more
- pp 13-18
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TLDR
In the proposed system, classification of keywords by higher ranking of topics has contributed to an active role for the extraction of summarization, the results of summary ratio in social web is 40%-50%.Abstract:
The proposed system discuss a text summarization system over the social web site. The proposed system works by assigning scores to sentences in the document to be summarized, and using the highest ranking sentences in the summary. Highest ranking values are based on features extracted from the sentence. A linear combination of feature scores is used. In addition to basic summarization, some attempt is made to address the issue of targeting the text at the user. The intended user is considered to have little background knowledge or reading ability. The system helps by simplifying the individual words used in the summary. In the proposed system, classification of keywords by higher ranking of topics has contributed to an active role for the extraction of summarization, the results of summarization ratio in social web is 40%-50%.read more
Citations
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Journal ArticleDOI
T-BERTSum: Topic-Aware Text Summarization Based on BERT
TL;DR: Zhang et al. as mentioned in this paper proposed a topic-aware extractive and abstractive summarization model named T-BERTSum, based on Bidirectional Encoder Representations from Transformers (BERTs), which can simultaneously infer topics and generate summarization from social texts.
Book ChapterDOI
Sports News Generation from Live Webcast Scripts Based on Rules and Templates
TL;DR: The system extracts the important events occurring in the time period from the live webcast scripts according to the rules, and on the other hand, the system generates a brief summary from the Live Webcast scripts about the football matches.
Proceedings ArticleDOI
A Framework for Detection and Identification the Components of Arguments in Arabic Legal Texts
TL;DR: The framework is a collection of Iraq's Federal Court of Cassation decision documents with different binary classifiers based on relevant features to detect and identify the components of arguments from legal decisions texts as a final goal.
References
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