نتایج جستجو برای: summarization evaluation technique
تعداد نتایج: 1396390 فیلتر نتایج به سال:
A task-based evaluation scheme has been adopted as a new method of evaluation for automatic text summarization systems. It evaluates the performance of a summarization system in a given task, such as information retrieval and text categorization. This paper compares ten different summarization methods based on information retrieval tasks. In order to evaluate the system performance, the subject...
Summarization of software artifacts is an ongoing field of research among the software engineering community due to the benefits that summarization provides like saving of time and efforts in various software engineering tasks like code search, duplicate bug reports detection, traceability link recovery, etc. Summarization is to produce short and concise summaries. The paper presents the review...
The need to extract and manage vital information contained in copious volumes of text documents has given birth several automatic summarization (ATS) approaches. ATS found application academic research, medical health records analysis, content creation search engine optimization, finance media. This study presents a boundary-based tokenization method for extractive summarization. proposed perfo...
Document Summarization is a technique, which reduces the size of the documents and gives the outline and crisp information about the given group of documents. This paper introduces a new update summarization algorithm incorporating association rule mining and correlated concept based hierarchical clustering for dynamic environment. In this algorithm, the associated concepts are extracted using ...
This paper proposes an automatic speech summarization technique for English. In our proposed method, a set of words maximizing a summarization score indicating appropriateness of summarization is extracted from automatically transcribed speech and concatenated to create a summary. The extraction process is performed using a Dynamic Programming (DP) technique according to a target compression ra...
We present SumBasic+, a powerful multi-document summarization system built from first principles. SumBasic+ is designed as a baseline system to gauge the level of summarization results we could obtain using simple statistical techniques. Our extractive summarization system is based on word frequency statistics similar to the SumBasic method. Nevertheless, we were able to considerably improve it...
We present an application of ambiguity packing and stochastic disambiguation techniques for Lexical-Functional Grammars (LFG) to the domain of sentence condensation. Our system incorporates a linguistic parser/generator for LFG, a transfer component for parse reduction operating on packed parse forests, and a maximum-entropy model for stochastic output selection. Furthermore, we propose the use...
Automatic text summarization is a process to reduce the volume of text documents using computer programs to create a text summary with keeping the key terms of the documents. Due to cumulative growth of information and data, automatic text summarization technique needs to be applied in various domains. The approach helps in decreasing the quantity of the document without changing the context of...
Video summarization offers an intelligent way of reducing the video content temporally to satisfy the user preference for a shorter viewing time, and/or to meet the bit budget limitations in storage/communications. In this paper we formulate the summarization problem as a rate-distortion optimization problem under bit budget and frame skip constraints, and introduce a summarization distortion m...
The goal of Temporal Summarization task is to develop systems which can detect useful, new, and timely sentence-length updates about a developing event. This paper describes our participation in Temporal Summarization track of TREC2015. Based on the word embedding technique, we submitted two runs for the summarization task. The query expanding technique is used for the first run and relevant se...
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