Many online news/blog aggregators like Google, Yahoo and MSN allow users to browse/search many hundreds of news sources. This results in dozens, often hundreds, of stories about the same event. While the news aggregators cluster these stories, allowing the user to efficiently scan the major news items at any given time, they do not currently allow alternative browsing mechanisms within the clusters. Furthermore, their intra-cluster ranking mechanisms are often based on a notion of authority/popularity of the source. In many cases, this leads to the classic power law phenomenon -- the popular stories/sources are the ones that are already popular/authoritative, thus reinforcing one dominant viewpoint. Ideally, these aggregators would exploit the availability of the tremendous number of sources to identify the various dominant threads or viewpoints about a story and highlight these threads for the users. This paper presents an initial limited approach to such an interface: it classifies articles into two categories: fact and opinion. We show that the combination of (i) a classifier trained on a small (140K) training set of editorials/reports and (ii) an interactive user interface that ameliorates classification errors by re-ordering the presentation can be effective in highlighting different underlying viewpoints in a story-cluster. We briefly discuss the classifier used here, the training set and the UI and report on some initial anecdotal user feedback and evaluation.

Extractive summarization techniques cannot generate document summaries shorter than a single sentence, something that is often required. An ideal summarization system would understand each document and generate an appropriate summary directly from the results of that understanding. A more practical approach to this problem results in the use of an approximation: viewing summarization as a problem analogous to statistical machine translation. The issue then becomes one of generating a target document in a more concise language from a source document in a more verbose language. This paper presents results on experiments using this approach, in which statistical models of the term selection and term ordering are jointly applied to produce summaries in a style learned from a training corpus.

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.

This paper discusses passage extraction approaches to multidocument summarization that use available information about the document set as a whole and the relationships between the documents to build on single document summarization methodology.Multi-document summarization diers from single in that the issues of compression, speed, redundancy and passage selection are critical in the formation of useful summaries, as well as the user's goals in creating the summary.Our approach addresses these issues by using domain-independent techniques based mainly on fast, statistical processing, a metric for reducing redundancy and maximizing diversity in the selected passages, and a modular framework to allow easy parameterization for dierent genres, corpora characteristics and user requirements.We examined how h umans create multi-document summaries as well as the characteristics of such summaries and use these summaries to evaluate the performance of various multidocument summarization algorithms.

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 Stemming and its effects on TFIDF ranking (poster session) Share on Authors: Mark Kantrowitz Just Research, 4616 Henry Street, Pittsburgh, PA Just Research, 4616 Henry Street, Pittsburgh, PAView Profile , Behrang Mohit School of Computer Science, Carnegie Mellon University, Pittsburgh, PA School of Computer Science, Carnegie Mellon University, Pittsburgh, PAView Profile , Vibhu Mittal School of Computer Science, Carnegie Mellon University, Pittsburgh, PA School of Computer Science, Carnegie Mellon University, Pittsburgh, PAView Profile Authors Info & Claims SIGIR '00: Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrievalJuly 2000 Pages 357–359https://doi.org/10.1145/345508.345650Online:01 July 2000Publication History 28citation101DownloadsMetricsTotal Citations28Total Downloads101Last 12 Months51Last 6 weeks6 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 SiteGet Access

This paper describes a machine learning approach to building an efficient and accurate name spotting system. Finding names in free text is an important task in many text‐based applications. Most previous approaches were based on hand‐crafted modules encoding language and genre‐specific knowledge. These approaches had at least two shortcomings: They required large amounts of time and expertise to develop and were not easily portable to new languages and genres. This paper describes an extensible system that automatically combines weak evidence from different, easily available sources: parts‐of‐speech tags, dictionaries, and surface‐level syntactic information such as capitalization and punctuation. Individually, each piece of evidence is insufficient for robust name detection. However, the combination of evidence, through standard machine learning techniques, yields a system that achieves performance equivalent to the best existing hand‐crafted approaches.

Article Free Access Share on Summarizing text documents: sentence selection and evaluation metrics Authors: Jade Goldstein Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PAView Profile , Mark Kantrowitz Just Research, 4616 Henry Street, Pittsburgh, PA Just Research, 4616 Henry Street, Pittsburgh, PAView Profile , Vibhu Mittal Just Research, 4616 Henry Street, Pittsburgh, PA Just Research, 4616 Henry Street, Pittsburgh, PAView Profile , Jaime Carbonell Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA Language Technologies Institute, 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 121–128https://doi.org/10.1145/312624.312665Published:01 August 1999Publication History 264citation3,037DownloadsMetricsTotal Citations264Total Downloads3,037Last 12 Months222Last 6 weeks42 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 Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

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Most complex systems provide some form of help facilities. However, typically, such help facilities do not allow users to ask follow up questions or request further elaborations when they are not satisfied with the systems' initial offering. One approach to alleviating this problem is to present the user with a menu of possible follow up questions at every point. Limiting follow up information requests to choices in a menu has many advantages, but there are also a number of issues that must be dealt with in designing such a system. To dynamically generate useful embedded menus, the system must be able to, among other things, determine the context of the request, represent and reason about the explanations presented to the user, and limit the number of choices presented in the menu. This paper discusses such issues in the context of a patient education system that generates a natural language description in which the text is directly manipulable - clicking on portions of the text causes the system to generate menus that can be used to request elaborations and further information.

Examples are often used along with textual descriptions to help convey particular ideas - especially in instructional or explanatory contexts. These accompanying examples reflect information in the surrounding text, and in turn, also influence the text. Sometimes, examples replace possible (textual) elaborations in the description. It is thus clear that if object descriptions are to be generated, the system must incorporate strategies to handle examples. In this work, we shall investigate some of these issues in the generation of object descriptions.

A framework is presented for distributed problem solving. As an illustration of this system, the implementation of a job scheduler to do load balancing in distributed systems is developed and discussed. The framework is developed around a distributed blackboard system with multiple planes in a hierarchy. Sites in a distributed system have an incorrect and inconsistent picture of the states of other sites in the system. In this framework of cooperating specialists communicating by means of distributed blackboards, it is shown how to handle uncertain and unreliable knowledge. Some of the specialists used in the system, results obtained, and directions for future research are described.>