[PDF] Top 20 Context Based Similarity Analysis for Document Summarization
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Context Based Similarity Analysis for Document Summarization
... a context-sensitive document indexing model. The PageRank-based algorithm is applied for implementing the iteratively compute and how informative is each document ...Sentence similarity ... See full document
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Sentence Position revisited: A robust light weight Update Summarization ‘baseline’ Algorithm
... each document in chronological order until the length requirement is ...single document summariza- tion took place in DUC 2001. For multi-document summarization, first N words of the most ... See full document
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Automatic Text Document Summarization
... human analysis due to the fact that hundreds of pages of search results are generated for most input ...Thus document retrieval is not enough and we need a second level of abstraction to reduce this huge ... See full document
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Discourse indicators for content selection in summarization
... on summarization, graph models of text have been proposed that do not rely on dis- ...lexical similarity between sen- tences is used to induce graph structure (Erkan and Radev, 2004; Mihalcea and Tarau, ... See full document
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Text Summarization using Centrality Concept
... summarization [14, ...text summarization such as MEAD summarization ...centrality based on some centrality measures such as degree and lexis ...the similarity relation between pairs of ... See full document
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Improved Algorithms for Document Classification &Query-based Multi-Document Summarization
... for Summarization The basic algorithm is same as that described in ...cosine similarity between sentences, the sentences were expressed as vectors where the elements of the vectors consisted of only the ... See full document
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Graph based Neural Multi Document Summarization
... From Table 3, we observe that our GCN sys- tem significantly outperforms the commonly used baselines and traditional graph approaches such as Centroid, LexRank, and G-Flow. This indi- cates the advantage of the ... See full document
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Online Full Text
... a context of a document we understand its description (representation) enabling a quick and simple identification of the content of the document and allowing some additional operations on the ... See full document
5
Using Context Inference to Improve Sentence Ordering for Multi document Summarization
... source document sets and four manual summaries for each document set in its ...Each document set consists of 10 ...manual summarization sentence, we find a sentence in source document ... See full document
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Improving the Similarity Measure of Determinantal Point Processes for Extractive Multi Document Summarization
... the context of multi-document ...semantic similarity tasks: semantic textual simi- larity (STS; Cer et ...single- document summarization ... See full document
12
Summarization Approaches Based on Document Probability Distributions
... the document if its probability distribution is similar to that of the original ...our analysis using datasets from Document Understanding Conference (DUC), studying the results of unigram ... See full document
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Topic-based Multi-Document Summarization with Probabilistic Latent Semantic Analysis
... based on the identification of topics (or thematic foci) to construct generic or query-focused summaries. Of- ten, thematic features rely on identifying and weight- ing important keywords [21], or creating topic ... See full document
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The Benchmark of Paragraph and Sentence Extraction Summaries on Outlier Document Filtering Applied Multi-Document Summarizer
... There are lots of experiments done on DUC [27] and TAC (Text Analysis Conference) [28] data sets. The best ROUGE metrics obtained are known by the Kumar and his colleagues work [29]. They also shared and compared ... See full document
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An Unsupervised Multi Document Summarization Framework Based on Neural Document Model
... the document set, sentence filtering and beam- search are added ...making document model work well as document model may be weak in modeling noisy sentences with rare words or in bad ... See full document
10
Information Retrieval and Context Based Document Summarization Using Vector Space Model
... Text summarization is the process of automatically creating a compressed version of a given document preserving its information ...Automatic document summarization is an important research ... See full document
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Context Based Semantic Similarity and Document Retrieval
... Text based methods are extensively used in information retrieval on ...which context. Hence, it is an important task to find out the context of the words so as to effectively understand what the user ... See full document
5
Revisiting the Centroid based Method: A Strong Baseline for Multi Document Summarization
... In the experiments, we will therefore call this modification the ”global” variant of the centroid model. The same principle is used by the KL- Sum model (Haghighi and Vanderwende, 2009) in which the optimal summary ... See full document
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Sentence Similarity based on Dependency Tree Kernels for Multi document Summarization
... sentence similarity computation methods for MDS based on the dependency parse trees of the ...tree based sentence similarity kernels, which have originally been proposed for relation ...words ... See full document
6
Inducing Document Structure for Aspect based Summarization
... Pevzner and Hearst (2002)) (lower is better) which estimate the accuracy of segmentation boundaries, but do not evaluate whether a correct aspect has been assigned to any segment. Hence, we also in- clude aspect label ... See full document
11
OPTIMIZATION OF DOCUMENT CLUSTERING USING PHRASES
... keep the cluster sizes in a certain range, but it could be argued that forcing a limit on cluster size is not always desirable. A dynamic model for finding clusters irrelevant of their structure is CHAMELEON, which was ... See full document
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