Effectively Leveraging Entropy and Relevance for Summarization

Luo, Wenjuan; Zhuang, Fuzhen; He, Qianhua; Shi, Zhongzhi

doi:10.1007/978-3-642-17187-1_23

Cited by 4 publications

(6 citation statements)

References 9 publications

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“…Empirical correlation between sentence length and sentence entropy (calculated from matrix A) for four data-sets is shown in Row 1 of Table 1. As conjectured by Luo et al (2010), there is a high positive correlation between sentence length and sentence entropy in term space.…”

Section: (I) Sentence Entropy In Term Spacementioning

confidence: 75%

“…Normalizing the weights to obtain probability distribution, authors map summary words to the concepts and calculate entropy of the summary for quantitative assessment of its quality. Luo et al (2010) conjecture that sentence entropy proxies for coverage of information by the sentence. Authors consider sentence as a vector of terms (content words) in the document and compute probability distribution of terms, which is used for calculating entropy of the sentence.…”

Section: Entropy For Document Summarizationmentioning

confidence: 99%

“…Selecting sentences with high ζ t (S) favours inclusion of longer sentences in summary. Luo et al (2010) relate higher entropy of the sentence to higher coverage, thereby assuming that the summary with longer sentences covers more aspects in the document than shorter ones. However, this assumption may not hold if the document is not well written.…”

Section: Interpreting Sentence and Topic Entropymentioning

confidence: 99%

“…Existing extractive summarization methods rely on the intuitive notions of non-redundancy, relevance, and informativeness as signals of appositeness for inclusion of sentences in summary. While non-redundancy and relevance have been modeled in several earlier works (Luo et al, 2010;Alguliev et al, 2011;Gupta et al, 2014;Parveen et al, 2015;Nallapati et al, 2016Nallapati et al, , 2017Huang, 2017;Alguliyev et al, 2019;Saini et al, 2019), the notion of informativeness of a sentence is completely unattended. Despite sophisticated supervised and unsupervised techniques, existing approaches for document summarization are fundamentally devoid of any theory of importance.…”

Section: Introductionmentioning

confidence: 99%

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Investigating Entropy for Extractive Document Summarization

Khurana,

Bhatnagar

2021

Preprint

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Automatic text summarization aims to cut down readers' time and cognitive effort by reducing the content of a text document without compromising on its essence. Ergo, informativeness is the prime attribute of document summary generated by an algorithm, and selecting sentences that capture the essence of a document is the primary goal of extractive document summarization.In this paper, we employ Shannon's entropy to capture informativeness of sentences. We employ Non-negative Matrix Factorization (NMF) to reveal probability distributions for computing entropy of terms, topics, and sentences in latent space. We present an information theoretic interpretation of the computed entropy, which is the bedrock of the proposed E-Summ algorithm, an unsupervised method for extractive document summarization. The algorithm systematically applies information theoretic principle for selecting informative sentences from important topics in the document. The proposed algorithm is generic and fast, and hence amenable to use for summarization of documents in real time. Furthermore, it is domain-, collection-independent and agnostic to the language of the document. Benefiting from strictly positive NMF factor matrices, E-Summ algorithm is transparent and explainable too.We use standard ROUGE toolkit for performance evaluation of the proposed method on four well known public data-sets. We also perform quantitative as- *

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Section: (I) Sentence Entropy In Term Spacementioning

confidence: 75%

Section: Entropy For Document Summarizationmentioning

confidence: 99%

Section: Interpreting Sentence and Topic Entropymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Investigating Entropy for Extractive Document Summarization

Khurana,

Bhatnagar

2021

Preprint

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“…Based on entropy-related theories, we define various entropies, such as absolute alarm-message entropy, Term Frequency-Inverse Document Frequency (TF-IDF) alarm-message entropy, relative alarm-message entropy, self-information of monitoring alarm message, and average alarm-message entropy [32] to measure the importance of every piece of alarm message in order to obtain an overall picture of the content of the message. The absolute alarm-message entropy is defined from the perspective of the word frequency of a single sentence, while the TF-IDF alarm-message entropy and relative alarm-message entropy are defined considering the overall message base.…”

Section: Definition Of Alarm-message Entropiesmentioning

confidence: 99%

Intelligent Classification Method for Grid-Monitoring Alarm Messages Based on Information Theory

et al. 2019

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Alarm messages for grid monitoring are an important way to supervise the operation of power grids. Since the use of alarm messages is increasing exponentially due to the continuous expansion of the scale of power grids, a processing method for alarm messages based on statistics is proposed in this study. Entropy theory in information theory is introduced into the calculation of information value in power-grid alarming. By means of multiple entropy definitions, an evaluation index system for information value is constructed. Based on the analytic hierarchy process (AHP), various alarm-message entropies are used as indices to comprehensively assess the information value and level of each alarm message. Finally, an example is given to illustrate the effectiveness and practicality of the proposed method. This study provides a new idea for the intelligent classification of alarm messages.

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A New LSA and Entropy-Based Approach for Automatic Text Document Summarization

Yadav¹,

Sharan²

2018

International Journal on Semantic Web and Information Systems

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Automatic text document summarization is active research area in text mining field. In this article, the authors are proposing two new approaches (three models) for sentence selection, and a new entropy-based summary evaluation criteria. The first approach is based on the algebraic model, Singular Value Decomposition (SVD), i.e. Latent Semantic Analysis (LSA) and model is termed as proposed_model-1, and Second Approach is based on entropy that is further divided into proposed_model-2 and proposed_model-3. In first proposed model, the authors are using right singular matrix, and second & third proposed models are based on Shannon entropy. The advantage of these models is that these are not a Length dominating model, giving better results, and low redundancy. Along with these three new models, an entropy-based summary evaluation criteria is proposed and tested. They are also showing that their entropy based proposed models statistically closer to DUC-2002's standard/gold summary. In this article, the authors are using a dataset taken from Document Understanding Conference-2002.

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Effectively Leveraging Entropy and Relevance for Summarization

Cited by 4 publications

References 9 publications

Investigating Entropy for Extractive Document Summarization

Investigating Entropy for Extractive Document Summarization

Intelligent Classification Method for Grid-Monitoring Alarm Messages Based on Information Theory

A New LSA and Entropy-Based Approach for Automatic Text Document Summarization

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