Learning Correlations between Linguistic Indicators and Semantic Constraints: Reuse of Context-Dependent Descriptions of Entities

This paper presents the results of a study on the semantic constraints imposed on lexical choice by certain contextual indicators. We show how such indicators are computed and how correlations between them and the choice of a noun phrase description of a named entity can be automatically established using supervised learning. Based on this correlation, we have developed a technique for automatic lexical choice of descriptions of entities in text generation. We discuss the underlying relationship between the pragmatics of choosing an appropriate description that serves a specific purpose in the automatically generated text and the semantics of the description itself. We present our work in the framework of the more general concept of reuse of linguistic structures that are automatically extracted from large corpora. We present a formal evaluation of our approach and we conclude with some thoughts on potential applications of our method.Comment: 7 pages, uses colacl.sty and acl.bst, uses epsfig. To appear in the Proceedings of the Joint 17th International Conference on Computational Linguistics 36th Annual Meeting of the Association for Computational Linguistics (COLING-ACL'98

Use of Weighted Finite State Transducers in Part of Speech Tagging

This paper addresses issues in part of speech disambiguation using finite-state transducers and presents two main contributions to the field. One of them is the use of finite-state machines for part of speech tagging. Linguistic and statistical information is represented in terms of weights on transitions in weighted finite-state transducers. Another contribution is the successful combination of techniques -- linguistic and statistical -- for word disambiguation, compounded with the notion of word classes.Comment: uses psfig, ipamac

Building a Generation Knowledge Source using Internet-Accessible Newswire

In this paper, we describe a method for automatic creation of a knowledge source for text generation using information extraction over the Internet. We present a prototype system called PROFILE which uses a client-server architecture to extract noun-phrase descriptions of entities such as people, places, and organizations. The system serves two purposes: as an information extraction tool, it allows users to search for textual descriptions of entities; as a utility to generate functional descriptions (FD), it is used in a functional-unification based generation system. We present an evaluation of the approach and its applications to natural language generation and summarization.Comment: 8 pages, uses eps

LexRank: Graph-based Lexical Centrality as Salience in Text Summarization

We introduce a stochastic graph-based method for computing relative importance of textual units for Natural Language Processing. We test the technique on the problem of Text Summarization (TS). Extractive TS relies on the concept of sentence salience to identify the most important sentences in a document or set of documents. Salience is typically defined in terms of the presence of particular important words or in terms of similarity to a centroid pseudo-sentence. We consider a new approach, LexRank, for computing sentence importance based on the concept of eigenvector centrality in a graph representation of sentences. In this model, a connectivity matrix based on intra-sentence cosine similarity is used as the adjacency matrix of the graph representation of sentences. Our system, based on LexRank ranked in first place in more than one task in the recent DUC 2004 evaluation. In this paper we present a detailed analysis of our approach and apply it to a larger data set including data from earlier DUC evaluations. We discuss several methods to compute centrality using the similarity graph. The results show that degree-based methods (including LexRank) outperform both centroid-based methods and other systems participating in DUC in most of the cases. Furthermore, the LexRank with threshold method outperforms the other degree-based techniques including continuous LexRank. We also show that our approach is quite insensitive to the noise in the data that may result from an imperfect topical clustering of documents

Generating Natural Language Summaries from Multiple On-Line Sources: Language Reuse and Regeneration

The abundance of news wire on the World-Wide Web has resulted in at least four major problems, which seem to present the most interesting challenges to users and researchers alike: size,heterogeneity, change, and conflicting information. Size: several hundred newspapers and news agencies maintain their Web sites with thousands of news stories in each. Heterogeneity: some of the data related to news is in structured format (e.g., tables); more exists in semi-structured format (e.g.,Web pages, encyclopedias, textual databases); while the rest of the data is in textual form (e.g., newswire). Change: most Web sites and certainly all news sources change on a daily basis. Disagreement: different sources present conflicting or at least different views of the same event. We have approached the second, third, and fourth of these four problems from the point of view of text generation. We have developed a system, {\scsummons}, which when coupled with appropriate information extraction technology, generates a specific genre of natural language summaries of a particular event (which we call briefings) in a restricted domain. The briefings are concise, they contain facts from multiple and heterogeneous sources, and incorporate evolving information, highlighting agreements and contradictions among sources on the same topic. We have developed novel techniques and algorithms for combining data from multiple sources at the conceptual level (using natural language understanding), for identifying new information on a given topic; and for presenting the information in natural language form to the user. We named the framework that we have developed for these problems {\em language reuse and regeneration} (LRR). Its novelty lies in the ability to produce text by collating together text already written by humans on the Web. The main features of LRR are: increased robustness through a simplified parsing/generation component, leverage on text already written by humans, and facilities for the inclusion of structured data in computer-generated text. The present thesis contains an introduction to LRR and its use inmulti-document summarization. We have paid special attention to the techniquesfor producing conceptual summaries of multiple sources, to the creation and useof a LRR-based lexicon for text generation, to a methodology used to identifynew and old information in threads of documents, and to the generation offluent natural language text using all the components above. The thesis contains evaluations of the different components of {\sc summons} aswell as certain aspects of LRR as a methodology. A review of the relevantliterature is included as a separate chapter

Sentence simplification, compression, and disaggregation for summarization of sophisticated documents

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134176/1/asi23576.pd

Centroid-based summarization of multiple documents: sentence extraction, utility-based evaluation, and user studies

We present a multi-document summarizer, called MEAD, which generates summaries using cluster centroids produced by a topic detection and tracking system. We also describe two new techniques, based on sentence utility and subsumption, which we have applied to the evaluation of both single and multiple document summaries. Finally, we describe two user studies that test our models of multi-document summarization.Comment: 10 pages Corpus availability at http://perun.si.umich.edu/~radev/md