BACKGROUND To provide high-quality and safe care, clinicians must be able to optimally collect, distill, and interpret patient information. Despite advances in text summarization, only limited research exists on clinical summarization, the complex and heterogeneous process of gathering, organizing and presenting patient data in various forms. OBJECTIVE To develop a conceptual model for descri...

Most multi-document summarizers utilize term frequency related features to determine sentence importance. No empirical studies, however, have been carried out to isolate the contribution made by frequency information from that of other features. Here, we examine the impact of frequency on various aspects of summarization and the role of frequency in the design of a summarization system. We desc...

Extractive summarization typically uses sentences as summarization units. In contrast, joint compression and summarization can use smaller units such as words and phrases, resulting in summaries containing more information. The goal of compressive summarization is to find a subset of words that maximize the total score of concepts and cutting dependency arcs under the grammar constraints and su...

Text summarization is an important activity in the analysis of a high volume text documents. Text summarization has number of applications; recently number of applications uses text summarization for the betterment of the text analysis and knowledge representation. In this paper a frequent term based text summarization algorithm is designed and implemented in java. The designed algorithm works ...

As a result of the recent information explosion, there is an increasing demand for automatic summarization, and human abstractors often synthesize summaries that are based on sentences that have been extracted by machine. However, the quality of machine-generated summaries is not high. As a special application of information retrieval systems, the precision of automatic summarization can be imp...

The purpose of extractive summarization is to automatically select indicative sentences, passages, or paragraphs from an original document according to a certain target summarization ratio, and then sequence them to form a concise summary. In this paper, in contrast to conventional approaches, our objective is to deal with the extractive summarization problem under a probabilistic modeling fram...

Update summarization is an emerging summarization task of creating a short summary of a set of news articles, under the assumption that the user has already read a given set of earlier articles. In this paper, we propose a new co-ranking method to address the update summarization task. The proposed method integrates two co-ranking processes by adding strict constraints. In comparison with the o...

Although there has been a great deal of research on automatic summarization, most methods are based on a statistical approach, disregarding relationships between extracted textual segments. To ensure sentence connectivity, we propose a novel method to extract a set of comprehensible sentences that centers on several key points. This method generates a similarity network from documents with a le...

We present a novel unsupervised framework for focused meeting summarization that views the problem as an instance of relation extraction. We adapt an existing in-domain relation learner (Chen et al., 2011) by exploiting a set of task-specific constraints and features. We evaluate the approach on a decision summarization task and show that it outperforms unsupervised utterance-level extractive s...

Automatic Multi-Document summarization is still hard to realize. Under such circumstances, we believe, it is important to observe how humans are doing the same task, and look around for different strategies. We prepared 100 document sets similar to the ones used in the DUC multi-document summarization task. For each document set, several people prepared the following data and we conducted a sur...