Insertion of Ontological Knowledge to Improve Automatic Summarization Extraction Methods

Motta, Jesus Antonio; Capus, Laurence; Tourigny, Nicole

doi:10.4236/jilsa.2011.33015

Cited by 3 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some other summarisation methods employ ontologies to calculate similarity. According to the proposed method in Motta et al [16], a matrix is formed by the words of the sentences and is improved by adding a set of subtrees of Hypernymy and Hyponymy for each word. Rinaldi [17] introduced Semantic Relatedness Grade (SRG) for each pair of words in the document.…”

Section: Related Workmentioning

confidence: 99%

Similarity versus relatedness: A novel approach in extractive Persian document summarisation

Shafiee

Shamsfard

2017

Journal of Information Science

View full text Add to dashboard Cite

Automatic text summarisation is the process of creating a summary from one or more documents by eliminating the details and preserving the worthwhile information. This article presents a single/multi-document summariser using a novel clustering method for creating summaries. First, a feature selection phase is employed. Then, FarsNet, the Persian WordNet, is utilised to extract the semantic information of words. Therefore, the input sentences are categorised into three main clusters: similarity, relatedness and coherency. Each similarity cluster contains similar sentences to its core, while each relatedness cluster contains sentences that are related (but not similar) to its core. The coherency clusters show the sentences that should be kept together to preserve the coherency of the summary. Finally, the centroid of each similarity cluster having the most feature score is added to an empty summary. The summary is enlarged by including related sentences from relatedness clusters and excluding similar sentences to its content iteratively. Coherency clusters are applied to the created summary in the last step. The proposed method has been compared with three known existing text summarisation systems and techniques for the Persian language: FarsiSum, Parsumist and Ijaz. Our proposed method leads to improvement in experimental results on different measurements including precision, recall, F-measure, ROUGE-N and ROUGE-L.

show abstract

Section: Related Workmentioning

confidence: 99%

Similarity versus relatedness: A novel approach in extractive Persian document summarisation

Shafiee

Shamsfard

2017

Journal of Information Science

View full text Add to dashboard Cite

show abstract

“…This way of doing has been recently apply to automatic summarization process (Motta et al, 2011). In the present paper, we propose to enrich these first results by adding an experiment we conducted with methods for optimizing attribute space, well-known but not enough used in this field.…”

Section: Introductionmentioning

confidence: 99%

“…In fact, this attribute space is a matrix, where the rows represent the sentences of the documents and the columns are the words of these sentences. Each item of the matrix corresponds to the frequency of the word in the sentence in a vector space model (Motta et al, 2011). By applying one of the five methods on this matrix, we obtained an optimized attribute space composed of sentences with important information.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Efficiency of Linear Techniques to Optimize Attribute Space in Machine Learning: Relevant Results for Extractive Methods of Summarizing

Motta¹,

Capus²,

Tourigny³

2012

CIS

Self Cite

View full text Add to dashboard Cite

One major challenge in the field of machine learning, especially in classification problems, is to optimize the attribute space in order to obtain a classification function, which will be used to discriminate future items. Several approaches to optimize the attribute space can be used: some of them select the most relevant attributes and the other ones extract certain attributes to create a new smaller set of variables. These classification approaches have recently been implemented in the automatic summarization process with promising results. This paper enriches these first results with another new experiment. Five well-known linear methods were exploited to optimize the attribute space in an original manner on a corpus of 1250 text documents. These methods, used in data clustering and unsupervised machine learning, allow either attribute selection (Singular Value Decomposition, K-Means, Kohonen Neural Networks) or new attribute extraction (Principal Component Analysis, Factor Analysis). After having applied these methods to optimize attribute space, the validation phase was focused on the discrimination power of the obtained classification function. For that, six techniques of machine learning were used to abduce the classification function. Its performance was evaluated with the metric F mesure and ROC curves. The results show that the application of the five chosen linear methods for optimizing attribute space in the automatic summarization process by extraction is relevant. They also show which machine learning technique is preferable to use with each linear method to obtain a better efficiency.

show abstract

VENCE: A new machine learning method enhanced by ontological knowledge to extract summaries

Motta

Capus

Tourigny

2016

2016 SAI Computing Conference (SAI)

View full text Add to dashboard Cite

Insertion of Ontological Knowledge to Improve Automatic Summarization Extraction Methods

Cited by 3 publications

References 15 publications

Similarity versus relatedness: A novel approach in extractive Persian document summarisation

Similarity versus relatedness: A novel approach in extractive Persian document summarisation

Evaluation of Efficiency of Linear Techniques to Optimize Attribute Space in Machine Learning: Relevant Results for Extractive Methods of Summarizing

VENCE: A new machine learning method enhanced by ontological knowledge to extract summaries

Contact Info

Product

Resources

About