177 research outputs found
Querybiased text summarization as a question-answering technique
Abstract (these are discussed in Section 3). Segments with strong Text summarization may soon become a competitive method of answering queries asked of large text corpora. A query brings up a set of documents. These documents are then filtered by a summarizer: it constructs brief summaries from document fragments conceptually close to the query terms. We present the implementation of such a summarization system, based on lexical semantics, and discuss its operation. It is a configurable system, in the sense that the user will be able to choose among two or more implementations of every major step
Coreference-Based Summarization and Question Answering: a Case for High Precision Anaphor Resolution
Approaches to Text Summarization and Question Answering are known to benefit from the availability of coreference information. Based on an analysis of its contributions, a more detailed look at coreference processing for these applications will be proposed: it should be considered as a task of anaphor resolution rather than coreference resolution. It will be further argued that high precision approaches to anaphor resolution optimally match the specific requirements. Three such approaches will be described and empirically evaluated, and the implications for Text Summarization and Question Answering will be discussed
Investigating sentence weighting components for automatic summarisation
The work described here initially formed part of a triangulation exercise to establish the effectiveness of the Query Term Order algorithm. The methodology produced subsequently proved to be a reliable indicator of quality for summarising English web documents. We utilised the human summaries from the Document Understanding Conference data, and generated queries automatically for testing the QTO algorithm. Six sentence weighting schemes that made use of Query Term Frequency and QTO were constructed to produce system summaries, and this paper explains the process of combining and balancing the weighting components. We also examined the five automatically generated query terms in their different permutations to check if the automatic generation of query terms resulting bias. The summaries produced were evaluated by the ROUGE-1 metric, and the results showed that using QTO in a weighting combination resulted in the best performance. We also found that using a combination of more weighting components always produced improved performance compared to any single weighting component
Automated Text Summarization Base on Lexicales Chain and graph Using of WordNet and Wikipedia Knowledge Base
The technology of automatic document summarization is maturing and may
provide a solution to the information overload problem. Nowadays, document
summarization plays an important role in information retrieval. With a large
volume of documents, presenting the user with a summary of each document
greatly facilitates the task of finding the desired documents. Document
summarization is a process of automatically creating a compressed version of a
given document that provides useful information to users, and multi-document
summarization is to produce a summary delivering the majority of information
content from a set of documents about an explicit or implicit main topic. The
lexical cohesion structure of the text can be exploited to determine the
importance of a sentence/phrase. Lexical chains are useful tools to analyze the
lexical cohesion structure in a text .In this paper we consider the effect of
the use of lexical cohesion features in Summarization, And presenting a
algorithm base on the knowledge base. Ours algorithm at first find the correct
sense of any word, Then constructs the lexical chains, remove Lexical chains
that less score than other, detects topics roughly from lexical chains,
segments the text with respect to the topics and selects the most important
sentences. The experimental results on an open benchmark datasets from DUC01
and DUC02 show that our proposed approach can improve the performance compared
to sate-of-the-art summarization approaches
A graph-based approach towards automatic text summarization
Due to an exponential increase in number of electronic documents and easy access to information on the Internet, the need for text summarization has become obvious. An ideal summary contains important parts of the original document, eliminates redundant information and can be generated from single or multiple documents. There are several online text summarizers but they have limited accessibility and generate somewhat incoherent summaries. We have proposed a Graph-based Automatic Summarizer (GAUTOSUMM), which consists of a pre-processing module, control features and a post-processing module. For evaluation, two datasets, Opinosis and DUC 2007 are used and generated summaries are evaluated using ROUGE metrics. The results show that GAUTOSUMM outperforms the online text summarizers in eight out of ten topics both in terms of the summary quality and time performance. A user interface has also been built to collect the original text and the desired number of sentences in the summary.text summarizationgraph-basedautomatic text summarizationGAUTOSUM
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