Linguistic Features Usage in Single-Document Extractive Summarization
نویسنده
چکیده
Extractive summarizing can be divided into several steps. Preprocessing is a first of them and it usually includes: sentence splitting, stop words removal, stemming etc. In processing step, sentence features are calculated and then weights are assigned to these features using machine learning or heuristic methods. Those features are numerical characteristics of each sentence (e.g. sentence location, length, number of keywords, title similarity) which indicate the importance of sentence. In the end of processing stage final score of each sentence is calculated and highest score sentences are included in the summary. [1]
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