documents and in selecting apprw priate passages for text summarization. Preliminary results indicate some benefits for MMR diversity ranking in document retrieval and in single document summarization. The latter are borne out by the recent results of the SUMMAC conference in the evaluation of summarization

Existing methods for single document summarization usually make use of only the information contained in the specified document. This paper proposes the technique of document expansion to provide more knowledge to help single document summarization. A specified document is expanded to a small

an approach to retrieval based on probabilistic language modeling. We estimate models for each document individually. Our approach to modeling is non-parametric and integrates document indexing and document retrieval into a single model. One advantage of our approach is that collection statistics which

to Proposed Standard. RFC 2581 notes that the Fast Retransmit/Fast Recovery algorithm specified in that document does not recover very efficiently from multiple losses in a single flight of packets, and that RFC 2582 contains one set of modifications to address this problem.

Machine learning for text classification is the cornerstone of document categorization, news filtering, document routing, and personalization. In text domains, effective feature selection is essential to make the learning task efficient and more accurate. This paper presents an empirical comparison

In both e-business and e-science, we often need to integrate services across distributed, heterogeneous, dynamic “virtual organizations ” formed from the disparate resources within a single enterprise and/or from external resource sharing and service provider relationships. This integration can

In this article we propose a standard for role-based access control (RBAC). Although RBAC models have received broad support as a generalized approach to access control, and are well recognized for their many advantages in performing large-scale authorization management, no single authoritative

MUltilingual Sentence Extractor (MUSE) is aimed at multilingual single-document summarization. MUSE implements a supervised language-independent summarization approach based on optimization of multiple sentence ranking methods using a Genetic Algorithm. The main advantage of MUSE is its language

Existing methods for single document keyphrase extraction usually make use of only the information contained in the specified document. This paper proposes to use a small number of nearest neighbor documents to provide more knowledge to improve single document keyphrase extraction. A specified

This paper assesses different organizational forms in terms of their ability to generate information about investment projects and allocate capital to these projects efficiently. A decentralized approach–with small, single-manager firms–is most likely to be attractive when information about