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23rd Mexican International Conference on Artificial Intelligence

作者： Roldan-Ahumada, Judith-Agueda Avendano-Garrido, Martha-Lorena Torres-Moreno, Juan-Manuel Univ Veracruzana Fac Matemat Xalapa Veracruz Mexico Univ Avignon Lab Informat Avignon Avignon France

ISBN: (纸本)9783031755422;9783031755439

This paper presents CafeTAL, a new automatic text summarization algorithm by extraction. CafeTAL is based on a Transportation Network model combined with Word Mover's Distance using Word Embeddings. The document employed in the reported experiments is a French language document composed of two different topics, including polysemous words. In order to get significant statistics, our evaluation protocol is based on the ROUGE metric using a vast number (245) of human judges. Our results are very encouraging.

关键词： automatic text summarization Transportation Network Word Mover's Distances Optimal Transport Word Embedding

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A Novel Model of Generative automatic text summarization Based on BART

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2025年第2期52卷 507-514页

作者： Wang, Yahui Chang, Qingxia Meng, Xuelei Foreign Languages School Lanzhou Jiaotong University Gansu Lanzhou730070 China Traffic and Transportation School Lanzhou Jiaotong University Gansu Lanzhou730070 China

To obtain useful information accurately and quickly from the massive text information is the most urgent need for people nowadays. The text automatic summarization technology summarizes and condenses the given source text information and generates short texts that are concise, fluent and retain key information. This paper proposes a novel automatic summarization model - automatic summarization Model based on BART and Actor - Critic Algorithm (BART-ACA-AST) to realize the efficient text summarization processing. The ROUGE metric system is used to evaluate the similarity and correlation between the mechanically generated text summaries and the reference summaries to assess the quality of the listed models. BERTScore is used to appraise the semantic resemblance between the rewritten summary and the reference summary more concisely. The computing results demonstrate the excellent performance of the model. The method proposed in this article can serve as a reference for the automatic text summarization work. © (2025), (International Association of Engineers). All rights reserved.

关键词： actor-critic algorithm automatic text summarization BART BERTScore ROUGE

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automatic text summarization for government news reports based on multiple features

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JOURNAL OF SUPERCOMPUTING 2024年第3期80卷 3212-3228页

作者： Yang, Yanni Tan, Yiting Min, Jintao Huang, Zhengwei China Three Gorges Univ Sch Literature & Media Yichang Peoples R China China Three Gorges Univ Sch Comp & Informat Technol Yichang Peoples R China China Three Gorges Univ Sch Econ & Management Yichang Peoples R China

The purpose of government news summarization is to extract the most important information from official government news reports. It is important for readers to be able to understand government news quickly in the age of information overload. Compared with other types of news, government news reports have more detailed content and possess a more normative format, resulting in greater length. To resolve the contradiction between the length of these reports and the trend of fragmented reading, this research proposes an automatic text summarization model based on multiple features. First, the features are extracted using the TF-IDF algorithm and word vector embedding method based on the bidirectional encoder representation from the transformers model. Second, we score the sentences based on the position, keywords, and similarity features. Finally, the top-ranked sentence is selected to form the summarization. To verify the effectiveness and superiority of the proposed method, Edmundson and ROUGE were adopted. First, based on the Edmundson evaluation criteria, the summarization results of various methods were scored. The score differences between ATS summarization based on the proposed method and manual summarization were minimal across consistency, grammaticality, time sequence, conciseness, and readability, with values of 0.14, 0.18, 0.12, 0.10, and 0.16, respectively. This suggests that our summarization exhibits the highest similarity to manual summarization. Second, we evaluated the summarization results using the ROUGE evaluation criteria. The results indicate that the proposed method achieved significantly higher scores compared to other models. Specifically, for character-level ROUGE-1, the P, R, and F scores reached 0.84, 0.93, and 0.88, respectively. At the word-level ROUGE-1, the P, R, and F scores were 0.81, 0.89, and 0.85, respectively, which demonstrates a noticeable improvement over other models. Furthermore, compared to manual methods, the proposed method

关键词： Government news report automatic text summarization K-means TF-IDF

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automatic text summarization based on latent semantic indexing

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ARTIFICIAL LIFE AND ROBOTICS 2010年第1期15卷 25-29页

作者： Ai, Dongmei Zheng, Yuchao Zhang, Dezheng Univ Sci & Technol Sch Appl Sci Beijing Peoples R China Univ Sci & Technol Sch Informat Engn Beijing Peoples R China

automatic summarization is a topic of common concern in computational linguistics and information science, since a computer system of text summarization is considered to be an effective means of processing information resources. A method of text summarization based on latent semantic indexing (LSI), which uses semantic indexing to calculate the sentence similarity, is proposed in this article. It improves the accuracy of sentence similarity calculations and subject delineation, and helps the abstracts generated to cover the documents comprehensively as well as reducing redundancies. The effectiveness of the method is proved by the experimental results. Compared with the traditional keyword-based vector space model method of automatic text summarization, the quality of the abstracts generated was significantly improved.

关键词： automatic text summarization Latent semantic indexing Vector space model

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automatic text summarization in engineering information management

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10th International Conference on Asian Digital Libraries

作者： Zhan, Jiaming Loh, Han Tong Liu, Ying Sun, Aixin Natl Univ Singapore Dept Mech Engn Singapore 119077 Singapore Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Hong Kong Peoples R China Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore

ISBN: (纸本)9783540770930

In today's knowledge-intensive engineering environment, information management is an important and essential activity. However, existing researches of Engineering Information Management (EIM) mainly focused on numerical data such as computer models and process data. textual data, especially the case of free texts, which constitute a significant part of engineering information, have been somewhat ignored, mainly due to their lack of structure and the noisy information contained in them. Since summarization is a process to distill important information from source documents and at the same time remove iffelevant and redundant information, it could address the obstacles for handling textual data in EIM. Moreover, text summarization could address the increasing demand to integrate information from multiple documents and reduce the time in acquiring useful information from massive textual data in the engineering domain. This paper discusses in detail the need to apply text summarization in EIM and introduces a case study in summarizing multiple online customer reviews.

关键词： automatic text summarization engineering information management online customer reviews

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automatic text summarization Techniques Used in Industry 1st

Automatic Text Summarization Techniques Used in Industry

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1st International Conference on Emerging Trends in Information Technology (ICETIT)

作者： Kharita, Mukesh Kumar Singh, Pardeep Natl Inst Technol Hamirpur Himachal Prades India

ISBN: (纸本)9783030305772;9783030305765

automatic text summarization has very efficient techniques to create the summarization (important information form document) of the text document. As we know the information on the internet is growing rapidly and we cannot go through all the documents on the internet. If we want to read the news we have to identify which news we have to read then the news article can be summarized in only one paragraph by information extraction from the news article and by reading this short paragraph we can identify which article we should read. The automatic text summarization can be done by three methods. First one is the extraction method in which we just find the informative sentences from the whole document text. The second approach is the abstraction technique in which we find the meaning of the sentences and try to formulate the paragraph in the same meaning manner or we can say the same thing express the fewer words. The third and last approach is compression technique in which compress each sentence of the document. We compress the sentence by having the words related to the meaning of the sentence by title or first sentence of the graph. We can apply the hybrid approach to create the summarization in which we first compress the whole text document and then apply knowledge extraction. Knowledge extraction can be done by giving the score to each sentence and then pick the best k sentences according to compression ratio.

关键词： automatic text summarization Knowledge extraction summarization Hybrid approach

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automatic text summarization and it's Methods - A Review 6

Automatic Text Summarization and it's Methods - A Review

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6th International Conference on Cloud System and Big Data Engineering (Confluence)

作者： Bhatia, Neelima Jaiswal, Arunima Am Univ Noida Am Sch Engn & Technol Noida Uttar Pradesh India

ISBN: (纸本)9781467382038

text summarization is an incipient practice for verdict out the summary of the text article. text summarization has grew so uses such as Due to the enormous aggregate of information getting augmented on internet;it is challenging for the user to verve through altogether the information accessible on web. The large availability of internet content partakes constrained a broad research area in the extent of automatic text summarization contained by the Natural Language Processing (NLP), especially statistical machine learning communal. Terminated the bygone half a century, the defaulting has been addressed from numerous diverse standpoints, in erratic domains and using innumerable archetypes. In this survey paper we investigate the popular and important work done in the field of single and multiple document summarizations, generous distinctive prominence towards pragmatic approaches and extractive techniques. Particular auspicious slants that quintessence on unambiguous minutiae of the summarization are also deliberated. Exceptional consideration is ardent to involuntary assessment of summarization classifications, as forthcoming investigation on summarization is sturdily reliant over evolvement in this problem space.

关键词： Document summarization automatic text summarization Systematic review Survey Natural Language Processing.

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automatic text summarization by Mean-absolute Constrained Convex Optimization 41

Automatic Text Summarization by Mean-absolute Constrained Co...

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41st International Conference on Telecommunications and Signal Processing (TSP)

作者： Popescu, Claudiu Grama, Lacrimioara Rusu, Corneliu Tech Univ Cluj Napoca Fac Elect Telecommun & Informat Technol Signal Proc Grp Basis Elect Dept Cluj Napoca Romania

ISBN: (纸本)9781538646953

In this paper we propose a new algorithm for extractive text summarization. The proposed method is based on convex minimization and the properties of the l(1) norm. The algorithm has some advantages, like the extensibility and the ability to easy take into account additional information and constraints. It provides very good results, but its execution time is usually larger than that of other similar procedures.

关键词： automatic text summarization convex optimization l(1)-norm constrained optimization Numpy Scipy

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automatic text summarization by Non-topic Relevance Estimation 8

Automatic Text Summarization by Non-topic Relevance Estimati...

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8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (KDIR)

作者： Arroyo-Fernandez, Ignacio Torres-Moreno, Juan-Manuel Sierra, Gerardo Cabrera-Diego, Luis Adrian Univ Nacl Autonoma Mexico Inst Engn Mexico City DF Mexico UAPV Lab Informat Avignon Avignon France

ISBN: (纸本)9789897582035

We investigate a novel framework for automatic text summarization. In this framework underlying languageuse features are learned from a minimal sample corpus. We argue the low complexity of this kind of features allows relying in generalization ability of a learning machine, rather than in diverse human-abstracted summaries. In this way, our method reliably estimates a relevance measure for predicting summary candidature scores, regardless topics in unseen documents. Our output summaries are comparable to the state-of-the-art. Thus we show that in order to extract meaning summaries, it is not crucial what is being said;but rather how it is being said.

关键词： automatic text summarization Machine Learning Generalization Ability Regression Estimation

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automatic text summarization using Maximum Marginal Relevance for Health Ethics Protocol Document in Bahasa 13

Automatic Text Summarization using Maximum Marginal Relevanc...

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13th International Conference on Information and Communication Technology and System (ICTS)

作者： Purbawa, Doni Putra Malikhah Anggraini, Ratih Nur Esti Sarno, Riyanarto Inst Teknol Sepuluh Nopember ITS Fac Intelligent Elect & Informat Technol Informat Dept Surabaya Indonesia

ISBN: (纸本)9781665440592

The rapid development of science and technology in the health sector cannot be separated from the support of health research results. Before the research with human as a subject is conducted, every health research in Indonesia is required to make a health ethics protocol document in Bahasa which must comply with basic ethical principles. To determine whether the health research ethics protocol document has met the ethical principles, the health research ethics protocol document will be reviewed by a competent reviewer. The health research ethics protocol document consists of several parts and has a large number of pages, so to conduct a review, reviewers need a long time to understand and analyze the health research ethics protocol document. To reduce the review time, an automatic text summarization (ATS) is needed. ATS extracts important information in health research ethics protocol documents and presents it to reviewers. This research uses cosine similarity and Maximum Marginal Relevance (MMR) and textRank to summarize the document. The MMR method is considered to have more stable results than textRank based on the ROUGE evaluation results. The evaluation of result with the ROUGE Toolkit showed F-score value of 19.92% for document 1 and 10.98% for document 2 using MMR.

关键词： automatic text summarization bahasa ethics protocol document MMR health

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