[PDF] Top 20 A Supervised Aggregation Framework for Multi Document Summarization
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A Supervised Aggregation Framework for Multi Document Summarization
... a supervised aggregation summarization framework by combin- ing the results from four typical multi-document systems including LexPageRank, LexHITS, Manifold-ranking method and ... See full document
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System Combination for Multi document Summarization
... for multi-document summarization, the output of them are often available only on one dataset or even ...sophisticated supervised methods (Kulesza and Taskar, 2012; Cao et ... See full document
11
A Sentence Compression Based Framework to Query Focused Multi Document Summarization
... compression-based framework for query-specific ...the summarization models on the standard Document Understanding Confer- ence (DUC) 2006 and 2007 corpora 2 for ... See full document
11
Study on Multi Document Summarization by Machine Learning Technique for Clustered Documents
... Given a set of topically related documents, the segmentation process is carried out using a Bayesian framework. By using similar sentences from different documents more accurate segment likelihood estimations are ... See full document
5
Automatic Text Document Summarization
... the document first and scoring the individual sentences with respect to the ...in document summarization to discover the topics present in a document ...clustering-based summarization ... See full document
7
Towards Abstractive Multi Document Summarization Using Submodular Function Based Framework, Sentence Compression and Merging
... a multi-document setting aims at generating summaries by deeply understanding the contents of the document set and rewriting the most rel- evant information in natural ... See full document
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Deconstructing Human Literature Reviews – A Framework for Multi Document Summarization
... the framework, the objective was to compare its “human-ness” represented by its Comprehensibility, Readability and Usefulness against human-written literature reviews and machine-generated sentence ...the ... See full document
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Subtopic driven Multi Document Summarization
... DUC 2004: Note that our model STDS is de- signed to incorporate readers’ comments. When no comments are available, STDS/NC performs fairly well, as shown in Table 2. We observe that STDS/NC achieves comparable results as ... See full document
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Query-Focused Multi-Document Summarization Using Co-Training Based Semi-Supervised Learning
... Inspired by PageRank and HITS algorithm, much focus has been put on adopting graph- based ranking algorithm like LexRank (Erkan and Radev, 2004) and TextRank (Mihalcea and Tarau, 2004) to multi-document ... See full document
10
Content Selection in Multi-Document Summarization
... DPP: Determinantal point processes (DPPs) (Kulesza and Taskar, 2012) are prob- abilistic models of sets which balance the selection of important information and diverse groups of sentences within a given length. ... See full document
257
Adapting the Neural Encoder Decoder Framework from Single to Multi Document Summarization
... from multi- document ...from multi-document ...the multi-document input being repeat- edly used for summary ...from multi-document inputs and dynamically adjusts ... See full document
11
Multi Document Summarization of Evaluative Text
... in summarization, we chose to develop our own sentence extractor as a first attempt at summarizing evaluative argu- ...open-source framework for multi- document summarization, to suit ... See full document
8
Multi Document Summarization Using K Medoids Clustering Approach
... Abstract: Multi document summarization is the process of transforming a set of documents into a single summarized ...summarized document can give overall idea of the document ... See full document
5
An Unsupervised Multi Document Summarization Framework Based on Neural Document Model
... neural document model into multi-document summarization task and pro- posed a document-level reconstruction framework named ...this framework, we represent and reconstruct ... See full document
10
Query focused Multi Document Summarization: Combining a Topic Model with Graph based Semi supervised Learning
... Performance is presented at Table 2. We can find that ROUGE performance of one-layer graph rank- ing algorithms such as Manifold and LexRank, where topic information is neglected, achieve worse results than all two-layer ... See full document
11
Supervised Learning of Automatic Pyramid for Optimization Based Multi Document Summarization
... extractive summarization, there are several previous optimization-based ap- proaches which included an approximation of ROUGE in the objective function to ...a supervised setup, and subsequently employed ... See full document
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A Subjective Logic Framework for Multi Document Summarization
... SubSum is an evidence-based method. It formulates opinions using evidence derived or found in a document, which can be terms, phrases, sentences, co-occurrences of words or phrases, or any syntactic or semantic ... See full document
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A General Optimization Framework for Multi Document Summarization Using Genetic Algorithms and Swarm Intelligence
... Extractive multi-document summarization (MDS) is often cast as a discrete optimization problem where the document collection is considered as a set of sentences and the task is to select an ... See full document
11
From Single to Multi document Summarization
... (2) When it came to the measure for cohesion the results are confusing. If even the human- made summaries score only 2.74 out of 4, it is unclear what this category means, or how the assessors arrived at these scores. ... See full document
8
A Multi Document Multi Lingual Automatic Summarization System
... As it can be seen in table 2, TFIDF-based meth- ods score higher than SVD-based methods. Also, the most promising distance was the hamming dis- tance. It can also be seen that the performance de- creases substantially ... See full document
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