Automatic summarization evaluation is critical to the development of summarization systems. While ROUGE has been shown to correlate well with human evaluation for content match in text summarization, there are many characteristics in multiparty meeting domain, which may pose potential problems to ROUGE. In this paper, we carefully examine how well the ROUGE scores correlate with human evaluatio...

Text Summarization has been an extensively studied problem. Traditional approaches to text summarization rely heavily on feature engineering. In contrast to this, we propose a fully data-driven approach using feedforward neural networks for single document summarization. We train and evaluate the model on standard DUC 2002 dataset which shows results comparable to the state of the art models. T...

Multi-document summarization is the automatic production of a unique summary from a collection of texts. In this paper, we propose a statistical generative approach for multi-document summarization that combines simple information such as sentence position in the text and semantic-discursive information from CST (Cross-Document Structure Theory). In particular, we formulate the multi-document s...

Text summarization is basically summarizing of the given paragraph with use natural language processing and machine learning. There has been an explosion in quantity textual content records from lot sources. This a useful supply facts information which needs to be efficiently summarized useful. In this paper, primary tactics computerized were described. The distinctive approaches for effectiven...

Extractive summarization suffers from irrelevance, redundancy and incoherence. Existing work shows that abstractive rewriting for extractive summaries can improve the conciseness readability. These systems consider extracted as only input, which is relatively focused but lose important background knowledge. In this paper, we investigate contextualized rewriting, ingests entire original document...

OBJECTIVES This review examines work on automated summarization of electronic health record (EHR) data and in particular, individual patient record summarization. We organize the published research and highlight methodological challenges in the area of EHR summarization implementation. TARGET AUDIENCE The target audience for this review includes researchers, designers, and informaticians who ...

Due to the overabundance of textual information on-line, automatic text summarization [Saggion and Poibeau, 2013], the reduction of a text to its essential content, is fundamental for information systems dealing with textual content. In recent years text summarization research has intensified with well known evaluation programmes promoting the interest in the area. Comparison of different summa...

Existing multi-document summarization systems usually rely on a specific summarization model (i.e., a summarization method with a specific parameter setting) to extract summaries for different document sets with different topics. However, according to our quantitative analysis, none of the existing summarization models can always produce high-quality summaries for different document sets, and e...