A Four Dimension Graph Model for Automatic Text Summarization

Ferreira, Rafael; Cabral, Luciano de Souza; Lins, Rafael Dueire; Lima, Rinaldo; Franca, Gabriel; Simskez, Steven J.; Favaro, Luciano

doi:10.1109/wi-iat.2013.55

Cited by 32 publications

(17 citation statements)

References 12 publications

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“…But, the proposed method in this paper has been evaluated with three model summaries of each test document. The remarkable point is that human generated model summaries were also used for English text summarization methods despite the existence of benchmark dataset [48,49] and for other languages where there was no benchmark dataset [6,7,20]. At last, ROUGE [50] has been applied, a widely used metric, to evaluate the automatically generated summaries of our proposed method.…”

Section: Datasetmentioning

confidence: 99%

An Innovative Approach of Bangla Text Summarization by Introducing Pronoun Replacement and Improved Sentence Ranking

Haque¹,

Pervin²,

Begum³

2017

J Inf Process Syst

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This paper proposes an automatic method to summarize Bangla news document. In the proposed approach, pronoun replacement is accomplished for the first time to minimize the dangling pronoun from summary. After replacing pronoun, sentences are ranked using term frequency, sentence frequency, numerical figures and title words. If two sentences have at least 60% cosine similarity, the frequency of the larger sentence is increased, and the smaller sentence is removed to eliminate redundancy. Moreover, the first sentence is included in summary always if it contains any title word. In Bangla text, numerical figures can be presented both in words and digits with a variety of forms. All these forms are identified to assess the importance of sentences. We have used the rule-based system in this approach with hidden Markov model and Markov chain model. To explore the rules, we have analyzed 3,000 Bangla news documents and studied some Bangla grammar books. A series of experiments are performed on 200 Bangla news documents and 600 summaries (3 summaries are for each document). The evaluation results demonstrate the effectiveness of the proposed technique over the four latest methods.

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Section: Datasetmentioning

confidence: 99%

An Innovative Approach of Bangla Text Summarization by Introducing Pronoun Replacement and Improved Sentence Ranking

Haque¹,

Pervin²,

Begum³

2017

J Inf Process Syst

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show abstract

“…These two methods are simple and ignore some effective factors, such as semantic information and linguistic knowledge. Above all [30,31], these methods are all considered more similar to judge the relevance between nodes. Corresponding solutions of these limitations are proposed in this paper.…”

Section: Graph Sorting Algorithmmentioning

confidence: 99%

Text Summarization Using FrameNet-Based Semantic Graph Model

Han

Hu³

et al. 2016

Scientific Programming

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Text summarization is to generate a condensed version of the original document. The major issues for text summarization are eliminating redundant information, identifying important difference among documents, and recovering the informative content. This paper proposes a Semantic Graph Model which exploits the semantic information of sentence using FSGM. FSGM treats sentences as vertexes while the semantic relationship as the edges. It uses FrameNet and word embedding to calculate the similarity of sentences. This method assigns weight to both sentence nodes and edges. After all, it proposes an improved method to rank these sentences, considering both internal and external information. The experimental results show that the applicability of the model to summarize text is feasible and effective. Abstract Unsupervised Sentence classification Sentence ranking SVM HMM CRF Cluster-based Graph-based Supervised ManmadeSubmit your manuscripts at

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“…Even then, none of the existing lemmatization algorithm can yield 100 % accurate result. These inappropriate base words generated will affect accuracy of dependency parsing [2], which will directly affect the identification of the relation between the concepts to provide a meaningful knowledge [3][4][5]. As a result it will eventually affect the overall accuracy of semantic knowledge the knowledge base trying to represent [6].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Learning for Lemmatization in Morphology Analysis

Ting

Kadir

Sembok

et al. 2015

Communications in Computer and Information Science

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Abstract. Morphological analysis is used to study the internal structure words by reducing the number of vocabularies used while retaining the semantic meaning of the knowledge in NLP system. Most of the existing algorithms are focusing on stemmatization instead of lemmatization process. Even with technology advancement, yet none of the available lemmatization algorithms able to produce 100 % accurate result. The base words produced by the current algorithm might be unusable as it alters the overall meaning it tried to represent, which will directly affect the outcome of NLP systems. This paper proposed a new method to handle lemmatization process during the morphological analysis. The method consists three layers of lemmatization process, which incorporate the used of Stanford parser API, WordNet database and adaptive learning technique. The lemmatized words yields from the proposed method are more accurate, thus it will improve the semantic knowledge represented and stored in the knowledge base.

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A Four Dimension Graph Model for Automatic Text Summarization

Cited by 32 publications

References 12 publications

An Innovative Approach of Bangla Text Summarization by Introducing Pronoun Replacement and Improved Sentence Ranking

An Innovative Approach of Bangla Text Summarization by Introducing Pronoun Replacement and Improved Sentence Ranking

Text Summarization Using FrameNet-Based Semantic Graph Model

Adaptive Learning for Lemmatization in Morphology Analysis

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