In this paper, we propose a generic text summarization method that generates summaries of Turkish texts by ranking sentences according to their scores. Sentence scores are calculated using their surface level features, and summaries are created by extracting the highest ranked sentences from the original documents. In order to extract sentences which form a summary with an extensive coverage of the main content of the text and less redundancy, we use features such as term frequency, key phrase, centrality, title similarity and sentence position. The sentence rank is computed using a score function that uses its feature values and the weights of the features. The best feature weights are learned using machine learning techniques with the help of human constructed summaries. Performance evaluation is conducted by comparing summarization outputs with manual summaries of two newly created Turkish data sets. This paper presents one of the first Turkish summarization systems, and its results are promising. We introduce the usage of key phrase as a surface level feature in text summarization, and we show the effectiveness of the centrality feature in text summarization. The effectiveness of the features in Turkish text summarization is also analyzed in detail.
Cığır et al. REF generated summaries by ranking sentences due to their scores calculated by combining the features such as term frequency, title similarity, key phrases, position of the sentence in the document, and centrality of the sentence.
Generic text summarization for Turkish
ISCIS
Computer Science