The TIPSTER Text Summarization Evaluation (SUMMAC) has established definitively that automatic text summarization is very effective in relevance assessment tasks. Summaries as short as 17% of full text length sped up decisionmaking by almost a factor of 2 with no statistically significant degradation in Fscore accuracy. SUMMAC has also introduced a new intrinsic method for automated evaluation ...

How do graph clustering techniques compare in terms of summarization power? How well can they summarize a million-node graph with a few representative structures? In this paper, we compare and contrast different techniques: METIS, LOUVAIN, SPECTRAL CLUSTERING, SLASHBURN, BIGCLAM, HYCOMFIT, and KCBC, our proposed k-core-based clustering method. Unlike prior work that focuses on various measures ...

Automatic text summarization is the process of reducing the text content and retaining the important points of the document. Generally, there are two approaches for automatic text summarization: Extractive and Abstractive. The process of extractive based text summarization can be divided into two phases: pre-processing and processing. In this paper, we discuss some of the extractive based text ...

The Document Understanding Conference (DUC) 2005 evaluation had a single useroriented, question-focused summarization task, which was to synthesize from a set of 25-50 documents a well-organized, fluent answer to a complex question. The evaluation shows that the best summarization systems have difficulty extracting relevant sentences in response to complex questions (as opposed to representativ...

the current work investigates a developed automatic Arabic text summarization model. In this model, a technique of word root clustering is used as the major activity. Unlike the previously presented systems of Arabic text summarization in the extract based design field, the current model adopts cluster weight of word roots instead of the word weight itself. The model is thoroughly illustrated t...

Summarization is a Process of filtering the most important information from source/sources for a particular user and task. Summarization is a very useful task which gives support to many other tasks. It takes advantage of the techniques developed for Natural Language Processing tasks. Multidocument summarization is a technique of summarize the multiple document into one paragraph. Multi-documen...

Abstractive summarization is the ultimate goal of document summarization research, but previously it is less investigated due to the immaturity of text generation techniques. Recently impressive progress has been made to abstractive sentence summarization using neural models. Unfortunately, attempts on abstractive document summarization are still in a primitive stage, and the evaluation results...

This paper evaluates the performance of different similarity measures in the context of document summarization. For this purpose in this paper a simple and effective sentence extractive technique is used. The proposed method is based on evaluation of relevance score of sentence. Many measures are available for the calculation of inter sentence relationships. To calculate a similarity between se...

Text summarizers automatically construct summaries of a naturallanguage document. This paper examines the use of text summarization within data mining, identifying the potential summarizers have for uncovering interesting and unexpected information. It describes the current state of the art in commercial summarization and current approaches to the evaluation of summarizers. The paper then propo...

In the period since 2004, many novel sophisticated approaches for generic multi-document summarization have been developed. Intuitive simple approaches have also been shown to perform unexpectedly well for the task. Yet it is practically impossible to compare the existing approaches directly, because systems have been evaluated on different datasets, with different evaluation measures, against ...