Temporal summarization of event-related updates in wikipedia

Géorgescu, Mihai; Pham, Dang Duc; Kanhabua, Nattiya; Zerr, Sergej; Siersdorfer, Stefan; Nejdl, Wolfgang

doi:10.1145/2487788.2487923

Cited by 18 publications

(7 citation statements)

References 12 publications

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“…During traversing, the COBWEB algorithm executes one of the four possible operations (insert, create, merge and split) based on maximizing the criterion function. Recently Georgescu et al (2013) have presented Wikipedia Event Reporter, a system that automatically extracts events from the Wikipedia revision history and presents related information summary to the user. A change in Wikipedia is meant by the updates occurred in one revision when compared to the previous revision of an article.…”

Section: Clustering Based Methodsmentioning

confidence: 99%

Summarization of Changes in Dynamic Text Collections

Kar

2013

Electronic Workshops in Computing

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Information Retrieval is the Informatics field primarily focused on all problems and challenges related to information storage and access. The large majority of works in this area are based on static collections of documents. However, many of these collections are dynamic, and have evolved over time with documents being added, edited or simply removed at different times. Even in highly dynamic environments such as the World Wide Web, research tends to be centered on the most recent version of the documents and all the past information is normally discarded. Recognizing these changes over dynamic text collections and exploiting them for document retrieval and presentation purposes introduce new and relevant research challenges. This paper addresses the opportunity that gains relevance in this context-summarization of changes in dynamic text collections. We first define the problem in order to produce a summary that describes textual changes to an entire document or a set of related documents over an user defined time period. Then, from literature we present an extensive overview of the relevant approaches depicting similar problems and at last some discussions including future aspects.

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Section: Clustering Based Methodsmentioning

confidence: 99%

Summarization of Changes in Dynamic Text Collections

Kar

2013

Electronic Workshops in Computing

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“…Referring to the time-based summarization as the task of temporal summarization, most of these systems focus on temporal expression extraction from text normalizing references to dates, times, and elapsed times [14]. The system in [26] generated the meaningful temporal summarization of event-related updates and automatically annotates the identified events in a timeline. Methods proposed in [27] retrieved sequential versions of a single web page during predefined time intervals.…”

Section: Topic Detection Andmentioning

confidence: 99%

Mining Sequential Update Summarization with Hierarchical Text Analysis

Zhang

Si²,

Ma³

et al. 2016

Mobile Information Systems

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The outbreak of unexpected news events such as large human accident or natural disaster brings about a new information access problem where traditional approaches fail. Mostly, news of these events shows characteristics that are early sparse and later redundant. Hence, it is very important to get updates and provide individuals with timely and important information of these incidents during their development, especially when being applied in wireless and mobile Internet of Things (IoT). In this paper, we define the problem of sequential update summarization extraction and present a new hierarchical update mining system which can broadcast with useful, new, and timely sentence-length updates about a developing event. The new system proposes a novel method, which incorporates techniques from topic-level and sentence-level summarization. To evaluate the performance of the proposed system, we apply it to the task of sequential update summarization of temporal summarization (TS) track at Text Retrieval Conference (TREC) 2013 to compute four measurements of the update mining system: the expected gain, expected latency gain, comprehensiveness, and latency comprehensiveness. Experimental results show that our proposed method has good performance.

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“…A framework (Lin & Sundaram, 2007) has been presented to analyze and summarize the temporal dynamics in the activity of blogs. More recently, the Wikipedia Event Reporter (Georgescu et al, 2013) was proposed. This system combines two tasks, event detection and temporal summarization using Wikipedia revision history as a source of data.…”

Section: Temporal Summarizationmentioning

confidence: 99%

“…Therefore, it is necessary to filter out the article's revisions during the pre-processing stage. The authors in Wikipedia Event Reporter (Georgescu et al, 2013) simply discarded the updates made by anonymous users to avoid most suspicious edits. However, this assumption does not seem reasonable since an anonymous user can also make positive contributions to an article.…”

Section: Filtering Inserted Vandalism and Reverted Revisionsmentioning

confidence: 99%

Summarization of changes in dynamic text collections using Latent Dirichlet Allocation model

Kar

Nunes²,

Ribeiro³

2015

Information Processing & Management

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a b s t r a c tIn the area of Information Retrieval, the task of automatic text summarization usually assumes a static underlying collection of documents, disregarding the temporal dimension of each document. However, in real world settings, collections and individual documents rarely stay unchanged over time. The World Wide Web is a prime example of a collection where information changes both frequently and significantly over time, with documents being added, modified or just deleted at different times. In this context, previous work addressing the summarization of web documents has simply discarded the dynamic nature of the web, considering only the latest published version of each individual document. This paper proposes and addresses a new challenge -the automatic summarization of changes in dynamic text collections. In standard text summarization, retrieval techniques present a summary to the user by capturing the major points expressed in the most recent version of an entire document in a condensed form. In this new task, the goal is to obtain a summary that describes the most significant changes made to a document during a given period. In other words, the idea is to have a summary of the revisions made to a document over a specific period of time. This paper proposes different approaches to generate summaries using extractive summarization techniques. First, individual terms are scored and then this information is used to rank and select sentences to produce the final summary. A system based on Latent Dirichlet Allocation model (LDA) is used to find the hidden topic structures of changes. The purpose of using the LDA model is to identify separate topics where the changed terms from each topic are likely to carry at least one significant change. The different approaches are then compared with the previous work in this area. A collection of articles from Wikipedia, including their revision history, is used to evaluate the proposed system. For each article, a temporal interval and a reference summary from the article's content are selected manually. The articles and intervals in which a significant event occurred are carefully selected. The summaries produced by each of the approaches are evaluated comparatively to the manual summaries using ROUGE metrics. It is observed that the approach using the LDA model outperforms all the other approaches. Statistical tests reveal that the differences in ROUGE scores for the LDA-based approach is statistically significant at 99% over baseline.

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Temporal summarization of event-related updates in wikipedia

Cited by 18 publications

References 12 publications

Summarization of Changes in Dynamic Text Collections

Summarization of Changes in Dynamic Text Collections

Mining Sequential Update Summarization with Hierarchical Text Analysis

Summarization of changes in dynamic text collections using Latent Dirichlet Allocation model

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