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This study proposes a text summarization model that simultaneously performs sentence extraction and compression. We translate the text summarization task into a problem of extracting a set of dependency subtrees in the document cluster. We also encode obligatory case constraints as must-link dependency constraints in order to guarantee the readability of(More)
The objective of this study was to evaluate the pharmacokinetic and pharmacodynamic interactions between the oral adsorbent AST-120 and triazolam. In this randomized, cross-over study, 12 healthy volunteers received a single oral dose of triazolam 0.25 mg alone or with AST-120 2 g given 0, 30 or 60 min before triazolam administration. The area under the(More)
Many epidemiological studies have shown that coffee consumption reduces the risk of type 2 diabetes mellitus (T2D), although the reasons as to why remain unclear. In this study we investigated the effect of caffeine on pancreatic beta-cell damage in rats using the diabetogenic agent, streptozotocin (STZ). Wistar rats were given intraperitoneal injections of(More)
We propose a new method for query-oriented extractive multi-document summarization. To enrich the information need representation of a given query, we build a co-occurrence graph to obtain words that augment the original query terms. We then formulate the sum-marization problem as a Maximum Coverage Problem with Knapsack Constraints based on word pairs(More)
This is an overview of the NTCIR-12 MobileClick-2 task (a sequel to 1CLICK in NTCIR-9 and NTCIR-10). In the MobileClick task, systems are expected to output a concise summary of information relevant to a given query and to provide immediate and direct information access for mobile users. We designed two types of MobileClick subtasks, namely, iUnit ranking(More)
We describe our two query-oriented summarization systems implemented for the NTCIR-9 1CLICK task. We regard a Question Answering problem as a summarization process. Both of the systems are based on the integer linear programming technique, and consist of an abstractive summarization model and a model ensuring to cover diversified aspects for answering(More)
We present a new morphological analysis model that considers semantic plausi-bility of word sequences by using a recurrent neural network language model (RNNLM). In unsegmented languages, since language models are learned from automatically segmented texts and inevitably contain errors, it is not apparent that conventional language models contribute to(More)
We propose a new method which enables the training of a kernelized structured output model. The struc-tured output learning can flexibly represent a problem, and thus is gaining popularity in natural language processing. Meanwhile the polynomial kernel method is effective in many natural language processing tasks, since it takes into account the combination(More)
We describe our query-oriented summarization system implemented for the NTCIR-10 1CLICK-2 task. Our system is purely based on a summarization method regarding the task as a summarization process. The system calculates relevant scores of terms for a given query, then extracts relevant part of sentences from input sources. For the calculation of relevant(More)