This paper discusses the design andperformance of a hierarchical proxy-cache designed to make Internet information systems scale better. The design was motivated by our earlier trace-driven simulation study of Internet traffic. We believe that the conventional wisdom, that the benefits

increases. This paper provides the complete survey, which gives a comparative study about the existing multi-Document summarization techniques. This study gives an overall view about the current research issues, recent methods for summarization, data set and metrics suitable for summarization. This frame

This thesis defines a set of program restructuring operations (refactorings) that support the design, evolution and reuse of object-oriented application frameworks. The focus of the thesis is on automating the refactorings in a way that preserves the behavior of a program. The refactorings

We present and compare two approaches to the task of summarizing evaluative arguments. The first is a sentence extractionbased approach while the second is a language generation-based approach. We evaluate these approaches in a user study and find that they quantitatively perform equally well

. The core of Ptolemy is a set of object-oriented class definitions that makes few assumptions about the system to be modeled; rather, standard interfaces are provided for generic objects and more specialized, application-specific objects are derived from these. A basic abstraction in Ptolemy is the Domain

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This paper discusses a text extraction approach to multidocument summarization that builds on single-document summarization methods by using additional, available in-i formation about the document set as a whole and the relationships between the documents. Multi-document summarization differs from

of context, we are focusing our efforts on the pieces of context that can be inferred automatically from sensors in a physical environment. We then present a conceptual framework that separates the acquisition and representation of context from the delivery and reaction to context by a contextaware

This paper presents a new approach to hierarchical reinforcement learning based on decomposing the target Markov decision process (MDP) into a hierarchy of smaller MDPs and decomposing the value function of the target MDP into an additive combination of the value functions of the smaller MDPs

We present a method to automatically generate a concise summary by identifying and synthe-sizing similar elements across related text from a set of multiple documents. Our approach is unique in its usage of language generation to reformulate the wording of the summary. 1