Multi-document summarization using closed patterns

Qiang, Jipeng; Chen, Ping; Ding, Wei; Xie, Fei; Wu, Xindong

doi:10.1016/j.knosys.2016.01.030

Cited by 49 publications

(28 citation statements)

References 19 publications

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“…Sentences with top score are selected to produce a summary. e authors in [38] presented a pattern-based method for news article summarization. e score of each sentence is found by adding the weights of covered patterns.…”

Section: Related Workmentioning

confidence: 99%

Summarizing Online Movie Reviews: A Machine Learning Approach to Big Data Analytics

Khan

Gul

Uddin

et al. 2020

Scientific Programming

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Information is exploding on the web at exponential pace, so online movie review is becoming a substantial information resource for online users. However, users post millions of movie reviews on regular basis, and it is not possible for users to summarize the reviews. Movie review classification and summarization is one of the challenging tasks in natural language processing. Therefore, an automatic approach is demanded to summarize the vast amount of movie reviews, and it will allow the users to speedily distinguish the positive and negative aspects of a movie. This study has proposed an approach for movie review classification and summarization. For movie review classification, bag-of-words feature extraction technique is used to extract unigrams, bigrams, and trigrams as a feature set from given review documents, and represent the review documents as a vector space model. Next, the Naïve Bayes algorithm is employed to classify the movie reviews (represented as a feature vector) into positive and negative reviews. For the task of movie review summarization, Word2vec feature extraction technique is used to extract features from classified movie review sentences, and then semantic clustering technique is used to cluster semantically related review sentences. Different text features are used to calculate the salience score of each review sentence in clusters. Finally, the top-ranked sentences are chosen based on highest salience scores to produce the extractive summary of movie reviews. Experimental results reveal that the proposed machine learning approach is superior than other state-of-the-art approaches.

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Section: Related Workmentioning

confidence: 99%

Summarizing Online Movie Reviews: A Machine Learning Approach to Big Data Analytics

Khan

Gul

Uddin

et al. 2020

Scientific Programming

View full text Add to dashboard Cite

show abstract

“…Different features such as term frequency, length of sentence, title of news article, proper nouns, and first sentence of news article are utilized to rank sentences in the news documents, and the high-ranked sentences are combined to create a summary. A pattern-based summarization method is introduced by [31] to score sentences in news documents by adding weights of the covered patterns.…”

Section: Related Workmentioning

confidence: 99%

Sentence Embedding Based Semantic Clustering Approach for Discussion Thread Summarization

et al. 2020

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Huge data on the web come from discussion forums, which contain millions of threads. Discussion threads are a valuable source of knowledge for Internet users, as they have information about numerous topics. The discussion thread related to single topic comprises a huge number of reply posts, which makes it hard for the forum users to scan all the replies and determine the most relevant replies in the thread. At the same time, it is also hard for the forum users to manually summarize the bulk of reply posts in order to get the gist of discussion thread. Thus, automatically extracting the most relevant replies from discussion thread and combining them to form a summary are a challenging task. With this motivation behind, this study has proposed a sentence embedding based clustering approach for discussion thread summarization. The proposed approach works in the following fashion: At first, word2vec model is employed to represent reply sentences in the discussion thread through sentence embeddings/sentence vectors. Next, K-medoid clustering algorithm is applied to group semantically similar reply sentences in order to reduce the overlapping reply sentences. Finally, different quality text features are utilized to rank the reply sentences in different clusters, and then the high-ranked reply sentences are picked out from all clusters to form the thread summary. Two standard forum datasets are used to assess the effectiveness of the suggested approach. Empirical results confirm that the proposed sentence based clustering approach performed superior in comparison to other summarization methods in the context of mean precision, recall, and F-measure.

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“…Kajian semasa yang menggunakan perwakilan CP oleh Qiang et al (2016) telah menunjukkan kebolehan perwakilan berdasarkan corak jika dibandingkan dengan kaedah berasaskan istilah yang lain dalam proses peringkasan dokumen yang pelbagai dan ia juga mengatasi (Baralis et al, 2012) dalam penilaian ROUGE-2 skor-F. Dengan menggunakan Corak Tertutup CP, ayat-ayat paling penting akan diekstrak dan ayat-ayat lewah akan dikurangkan. CP juga adalah perwakilan kompak bagi FSP yang tidak termasuk dalam FSP lain yang mempunyai nilai sokongan yang sama dan inilah yang membezakannya daripada MFS.…”

Section: Teknik Berdasarkan Corakunclassified

Model Peringkasan Teks Ekstraktif Dwibahasa menggunakan Fitur Kekangan Corak Tekstual (Bilingual Extractive Text Summarization Model using Textual Pattern Constraints)

Alias

Sainin

Mohammad

2020

gema

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In the era of digital information, an auto-generated summary can help readers to easily find important and relevant information. Most of the studies and benchmark data sets in the field of text summarization are in English. Hence, there is a need to study the potential of Malay language in this field. This study also highlights the problems in identifying and generating important information in extractive summaries. This is because existing text representation models such as BOW has weaknesses in inaccurate semantic representation, while the N-gram model has the issue of producing very high word vector dimensions. In this study, a bilingual text summarization model named MYTextSumBASIC has been developed to generate an extractive summary automatically in Malay and English. The MYTextSumBASIC summarizer model applies a text representation model known as FASP using three Textual Pattern Constraints, namely word item constraints, adjacent word constraints and sequence size constraints. There are three main phases in the framework of MYTextSumBASIC model, which are the development of the Malay language corpus, the development of MYTextSumBASIC model using FASP and the summary evaluation phase. In the summary evaluation phase, using the Malay language data sets of 100 news articles, the summaries produced by MYTextSumBASIC outperformed the summary generated by Baseline (Lead) and OTS summarizer with the highest average for retrieval (R) is 0.5849, precision (P) is 0.5736 and the F-score (Fm) is 0.5772. For manual evaluation by linguists, the MYTextSumBASIC method yielded a reading score of 4.1 and 3.87 for summary content generated using a random data set. Further experiments using the 2002 DUC English benchmark data set of 102 news articles have also shown that the MYTextSumBASIC model outperformed the best and lowest systems in the comparison with the mean retrieval values of ROUGE-1 (0.43896) and ROUGE-2 (0.19918). These findings conclude that the FASP text representation feature along with the textual pattern constraints used by our model can be used for bilingual text with competitive performance compared to other text summarization models.

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Multi-document summarization using closed patterns

Cited by 49 publications

References 19 publications

Summarizing Online Movie Reviews: A Machine Learning Approach to Big Data Analytics

Summarizing Online Movie Reviews: A Machine Learning Approach to Big Data Analytics

Sentence Embedding Based Semantic Clustering Approach for Discussion Thread Summarization

Model Peringkasan Teks Ekstraktif Dwibahasa menggunakan Fitur Kekangan Corak Tekstual (Bilingual Extractive Text Summarization Model using Textual Pattern Constraints)

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