A Survey of Unstructured Text Summarization Techniques <s> c) Supervised Approaches <s> ROUGE stands for Recall-Oriented Understudy for Gisting Evaluation. It includes measures to automatically determine the quality of a summary by comparing it to other (ideal) summaries created by humans. The measures count the number of overlapping units such as n-gram, word sequences, and word pairs between the computer-generated summary to be evaluated and the ideal summaries created by humans. This paper introduces four different ROUGE measures: ROUGE-N, ROUGE-L, ROUGE-W, and ROUGE-S included in the ROUGE summarization evaluation package and their evaluations. Three of them have been used in the Document Understanding Conference (DUC) 2004, a large-scale summarization evaluation sponsored by NIST. <s> BIB001 </s> A Survey of Unstructured Text Summarization Techniques <s> c) Supervised Approaches <s> This work presents a sentence ranking strategy based on distant supervision for the multi-document summarization problem. Due to the difficulty of obtaining large training datasets formed by document clusters and their respective human-made summaries, we propose building a training and a testing corpus from Wikinews. Wikinews articles are modeled as "distant" summaries of their cited sources, considering that first sentences of Wikinews articles tend to summarize the event covered in the news story. Sentences from cited sources are represented as tuples of numerical features and labeled according to a relationship with the given distant summary that is based on the Zipf law. Ranking functions are trained using linear regressions and ranking SVMs, which are also combined using Borda count. Top ranked sentences are concatenated and used to build summaries, which are compared with the first sentences of the distant summary using ROUGE evaluation measures. Experimental results obtained show the effectiveness of the proposed method and that the combination of different ranking techniques outperforms the quality of the generated summary. <s> BIB002
These approaches make use of human-made summaries or extracts to identify features or parameters of summarization algorithms. In these methods, a human user decides which parameters are important for text summary and accordingly the summary is generated. Bravo-Marquez and Manriquez trained ranking functions using linear regressions and ranking SVMs, which are also combined using Borda count BIB002 . Top ranked sentences are concatenated and used to build summaries, which are compared with the first sentences of the distant summary using ROUGE evaluation measures BIB001 . Experimental results obtained showed that the combination of different ranking techniques improves the quality of the generated summary.