Fuzzy Sets & Systems

Generating Update Summaries for DUC 2007


Update summaries as defined for the new DUC 2007 task deliver focused information to a user who has already read a set of older documents covering the same topic. In this paper, we show how to generate this kind of summary from the same data structure—fuzzy coreference cluster graphs—as all other generic and focused multi-document summaries. Our system ERSS 2007 implementing this algorithm also participated in the DUC 2007 main task, without any changes from the 2006 version.

An Initial Fuzzy Coreference Cluster Graph

Creating a Fuzzy Believer to Model Human Newspaper Readers

Montreal 2007


We present a system capable of modeling human newspaper readers. It is based on the extraction of reported speech, which is subsequently converted into a fuzzy theory-based representation of single statements. A domain analysis then assigns statements to topics. A number of fuzzy set operators, including fuzzy belief revision, are applied to model different belief strategies. At the end, our system holds certain beliefs while rejecting others.

Fuzzy Clustering for Topic Analysis and Summarization of Document Collections

Montreal 2007


Large document collections, such as those delivered by Internet search engines, are difficult and time-consuming for users to read and analyse. The detection of common and distinctive topics within a document set, together with the generation of multi-document summaries, can greatly ease the burden of information management. We show how this can be achieved with a clustering algorithm based on fuzzy set theory, which (i) is easy to implement and integrate into a personal information system, (ii) generates a highly flexible data structure for topic analysis and summarization, and (iii) also delivers excellent performance.

Processing of Beliefs extracted from Reported Speech in Newspaper Articles

A fuzzy believer?


The growing number of publicly available information sources makes it impossible for individuals to keep track of all the various opinions on one topic. The goal of our artificial believer system presented in this paper is to extract and analyze statements of opinion from newspaper articles.

Beliefs are modeled using a fuzzy-theoretic approach applied after NLP-based information extraction. A fuzzy believer models a human agent, deciding what statements to believe or reject based on different, configurable strategies.

Fuzzy Belief Revision


Fuzzy sets, having been the long-standing mainstay of modeling and manipulating imperfect information, are an obvious candidate for representing uncertain beliefs.

Unfortunately, unadorned fuzzy sets are too limited to capture complex or potentially inconsistent beliefs, because all too often they reduce to absurdities ("nothing is possible") or trivialities ("everything is possible").

However, we show that by combining the syntax of propositional logic with the semantics of fuzzy sets a rich framework for expressing and manipulating uncertain beliefs can be created, admitting Gärdenfors-style expansion, revision, and contraction operators and being moreover amenable to easy integration with conventional ``crisp'' information processing.

The model presented here addresses many of the shortcomings of traditional approaches for building fuzzy data models, which will hopefully lead to a wider adoptance of fuzzy technologies for the creation of information systems.


fuzzy belief revision, fuzzy information systems, soft computing, fuzzy object-oriented data model

Fuzzy Coreference Resolution for Summarization



We present a fuzzy-theory based approach to coreference resolution and its application to text summarization.

Automatic determination of coreference between noun phrases is fraught with uncertainty. We show how fuzzy sets can be used to design a new coreference algorithm which captures this uncertainty in an explicit way and allows us to define varying degrees of coreference.

The algorithm is evaluated within a system that participated in the 10-word summary task of the DUC 2003 competition.

Using Knowledge-poor Coreference Resolution for Text Summarization


We present a system that produces 10-word summaries based on the single summarization strategy of outputting noun phrases representing the most important text entities (as represented by noun phrase coreference chains). The coreference chains were computed using fuzzy set theory combined with knowledge-poor corefernce heuristics.

Supporting Reverse Engineering Tasks with a Fuzzy Repository Framework


Bad Honnef, the place to go!
Software reverse engineering (RE) is often hindered not by the lack of available data, but by an overabundance of it: the (semi-)automatic analysis of static and dynamic code information, data, and documentation results in a huge heap of often incomparable data. Additionally, the gathered information is typically fraught with various kinds of imperfections, for example conflicting information found in software documentation vs. program code.

Our approach to this problem is twofold: for the management of the diverse RE results we propose the use of a repository, which supports an iterative and incremental discovery process under the aid of a reverse engineer. To deal with imperfections, we propose to enhance the repository model with additional representation and processing capabilities based on fuzzy set theory and fuzzy belief revision.


fuzzy reverse engineering, meta model, extension framework, iterative process, knowledge evolution

Multi-ERSS and ERSS 2004


Last year, we presented a system, ERSS, which constructed 10 word summaries in form of a list of noun phrases. It was based on a knowledge-poor extraction of noun phrase coreference chains implemented on a fuzzy set theoretic base. This year we present the performance of an improved version, ERSS 2004 and an extension of the same basic system: Multi-ERSS constructs 100-word extract summaries for clusters of texts. With very few modifications we ran ERSS 2004 on Tasks 1 and 3 and Multi-ERSS on Tasks 2, 4, and 5, scoring generally above average in all but the linguistic quality aspects.

Agents and Databases: Friends or Foes?

Friendly Meetings in Vancouver


On first glance agent technology seems more like a hostile intruder into the database world. On the other hand, the two could easily complement each other, since agents carry out information processes whereas databases supply information to processes. Nonetheless, to view agent technology from a database perspective seems to question some of the basic paradigms of database technology, particularly the premise of semantic consistency of a database. The paper argues that the ensuing uncertainty in distributed databases can be modelled by beliefs, and develops the basic concepts for adjusting peer-to-peer databases to the individual beliefs in single nodes and collective beliefs in the entire distributed database.

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