This paper introduces a statistical model for query-relevant summarization: succinctly characterizing the relevance of a document to a query. Learning parameter values for the proposed model requires a large collection of summarized documents, which we do not have, but as a proxy, we use a collection of FAQ (frequently-asked question) documents. Taking a learning approach enables a principled, quantitative evaluation of the proposed system, and the results of some initial experiments---on a collection of Usenet FAQs and on a FAQ-like set of customer-submitted questions to several large retail companies---suggest the plausibility of learning for summarization.

Article Free Access Share on Agglomerative clustering of a search engine query log Authors: Doug Beeferman Lycos Inc., 400-2 Totten Pond Road, Waltham, MA Lycos Inc., 400-2 Totten Pond Road, Waltham, MAView Profile , Adam Berger School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PAView Profile Authors Info & Claims KDD '00: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data miningAugust 2000 Pages 407–416https://doi.org/10.1145/347090.347176Online:01 August 2000Publication History 499citation3,529DownloadsMetricsTotal Citations499Total Downloads3,529Last 12 Months149Last 6 weeks22 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

This paper investigates whether a machine can automatically learn the task of finding, within a large collection of candidate responses, the answers to questions. The learning process consists of inspecting a collection of answered questions and characterizing the relation between question and answer with a statistical model. For the purpose of learning this relation, we propose two sources of data: Usenet FAQ documents and customer service call-center dialogues from a large retail company. We will show that the task of “answer-finding” differs from both document retrieval and tradition question-answering, presenting challenges different from those found in these problems. The central aim of this work is to discover, through theoretical and empirical investigation, those statistical techniques best suited to the answer-finding problem.

We introduce OCELOT, a prototype system for automatically generating the “gist” of a web page by summarizing it. Although most text summarization research to date has focused on the task of news articles, web pages are quite different in both structure and content. Instead of coherent text with a well-defined discourse structure, they are more often likely to be a chaotic jumble of phrases, links, graphics and formatting commands. Such text provides little foothold for extractive summarization techniques, which attempt to generate a summary of a document by excerpting a contiguous, coherent span of text from it. This paper builds upon recent work in non-extractive summarization, producing the gist of a web page by “translating” it into a more concise representation rather than attempting to extract a text span verbatim. OCELOT uses probabilistic models to guide it in selecting and ordering words into a gist. This paper describes a technique for learning these models automatically from a collection of human-summarized web pages.

Article Free Access Share on Information retrieval as statistical translation Authors: Adam Berger School of Computer Science, Carnegie Mellon University, Pittsburgh, PA School of Computer Science, Carnegie Mellon University, Pittsburgh, PAView Profile , John Lafferty School of Computer Science, Carnegie Mellon University, Pittsburgh, PA School of Computer Science, Carnegie Mellon University, Pittsburgh, PAView Profile Authors Info & Claims SIGIR '99: Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrievalAugust 1999 Pages 222–229https://doi.org/10.1145/312624.312681Published:01 August 1999Publication History 393citation2,207DownloadsMetricsTotal Citations393Total Downloads2,207Last 12 Months125Last 6 weeks18 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

This paper describes a lightweight method for the automatic insertion of intra-sentence punctuation into text. Despite the intuition that pauses in an acoustic stream are a positive indicator for some types of punctuation, this work will demonstrate the feasibility of a system which relies solely on lexical information. Besides its potential role in a speech recognition system, such a system could serve equally well in non-speech applications such as automatic grammar correction in a word processor and parsing of spoken text. After describing the design of a punctuation-restoration system, which relies on a trigram language model and a straightforward application of the Viterbi algorithm, we summarize results, both quantitative and subjective, of the performance and behavior of a prototype system.

In this paper, we describe a large-scale investigation of dependency grammar language models. Our work includes several significant departures from earlier studies, notably a larger training corpus, improved model structure, different feature types, new feature selection methods, and more coherent training and test data. We report word error rate (wer) results of a speech recognition experiment, in which we used these models to rescore the output of the IBM speech recognition system. 1. INTRODUCTION One promising idea for advancing statistical language modeling is based upon dependency grammars, as reported in [4]. This work has the appeal of integrating, via the maximum entropy / minimum divergence (memd) technique, information from both syntax and ngrams. Moreover, unlike methods based upon context free grammars, grammatical information enters through the words themselves, rather than via abstract constituents like parse-tree node labels. A preliminary investigation of this idea, ju...

This paper introduces new methods based on exponential families for modeling the correlations between words in text and speech. While previous work assumed the effects of word co-occurrence statistics to be constant over a window of several hundred words, we show that their influence is nonstationary on a much smaller time scale. Empirical data drawn from English and Japanese text, as well as conversational speech, reveals that the "attraction" between words decays exponentially, while stylistic and syntactic contraints create a "repulsion" between words that discourages close co-occurrence. We show that these characteristics are well described by simple mixture models based on two-stage exponential distributions which can be trained using the EM algorithm. The resulting distance distributions can then be incorporated as penalizing features in an exponential language model.