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Multilingual Extractive Summarization: Investigating State-of-the-Art methods for English and Brazilian Portuguese 34th

Multilingual Extractive Summarization: Investigating State-o...

引用

34th Brazilian conference on Intelligent Systems

作者： Jorge, Germano Antonio Zani Bezerra, Davi Alves Xavier, Clarissa Castella Pardo, Thiago Alexandre Salgueiro Univ Sao Paulo Nucleo Interinstituc Linguist Computac NILC Inst Cencias Matemat & Computacao Sao Carlos SP Brazil SiDi Campinas SP Brazil

ISBN: (纸本)9783031790317;9783031790324

Automatic Text Summarization (ATS) is a natural language processing (NLP) task essential for handling large volumes of information. ATS can be classified into two main types: extractive and abstractive. Extractive summarization selects sentences or phrases directly from the source text(s), while abstractive summarization generates new sentences that try to capture the original meaning of the source text(s). This paper describes our efforts to perform extractive single-document summarization in multilingual contexts. Although various summarization methods, such as PreSumm and HiStruct+, have shown promising results on English corpora like CNN/DM, there is a significant gap in applying these methods to other languages, especially Brazilian Portuguese. Additionally, these summarizers were evaluated with traditional metrics like ROUGE, which has limitations as it primarily measures superficial text overlap. To fill these gaps, we evaluate the effectiveness of these state-of-the-art methods on the CSTNews corpus (with news texts in Brazilian Portuguese) employing ROUGE and the recent BLANC metric, which measures how much the generated summary aids a pre-trained language model (like BERT) in understanding the document. Our contributions include the results and comparison of adapted models, the discussion of the BLANC metric in contrast to ROUGE, and the expansion of resources available to the Portuguese and multilingual NLP community.

关键词： Extractive Summarization Multilinguality natural language processing

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Bridging the Emotional Gap: Google NL-AI Sentiment Analysis in Comparing Moroccan Literary Translations 8th

Bridging the Emotional Gap: Google NL-AI Sentiment Analysis ...

引用

8th International conference on Arabic language processing

作者： Refouh, Khadija Daoudi, Mimoune Princess Nourah Bint Abdulrahman Univ DISCRESOR Res Lab POB 84428 Riyadh Saudi Arabia Fac Letters & Human Sci Sais Fes BP 59 Fes Morocco

ISBN: (纸本)9783031791635;9783031791642

This preliminary study explores the potential of Google Artificial Intelligence based natural language (GNL-AI) sentiment analysis (SA) as a novel and complementary tool for comparing literary translations at the sentiment level. This study investigates how effectively GNL-AI can assess the emotional fidelity of each translation by analyzing the author's emotional sentiment conveyed in the text. It seeks to move beyond traditional methods often driven by subjectivity or literalness and instead explore a different perspective capable of assessing the emotional fidelity of each translation toward the source text (ST). Ahmed Toufiq's Arabic novel, "Abu Musa's Women Neighbors", is the ST that serves as a case study. This research analyzes the sentiment of two translations: English (TT1) by Roger Allen and French (TT2) by Philippe Vigreux. Analyzing the sentiment of 18 ST paragraphs and their corresponding segments in both translations TT1 and TT2 using GNL-AI revealed promising results. Most segments exhibited consistent sentiment patterns, highlighting the potential of this tool for identifying broad emotional trends in literary translations. Three paragraphs showed deviations in sentiment scores, highlighting challenges in achieving perfect alignment between the source text and the translations. Interestingly, the English translation captures well the Arabic author's intended emotional impact, showcasing the potential of GNL-AI SA in evaluating literary translation quality. In contrast, the French translation tends slightly towards neutrality, highlighting both the promise and the limitations of this approach. Future research will involve expanding the dataset and exploring how GNL-AI can be integrated with human expertise for a more comprehensive analysis.

关键词： literary translation sentiment analysis (SA) Google natural language AI (GNL-AI) emotional fidelity

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A Dual-Perspective Metaphor Detection Framework Using Large language Models

A Dual-Perspective Metaphor Detection Framework Using Large ...

引用

2025 IEEE International conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Lin, Yujie Liu, Jingyao Gao, Yan Wang, Ante Su, Jinsong School of Informatics Xiamen University Xiamen China

ISBN: (纸本)9798350368741

Metaphor detection, a critical task in natural language processing, involves identifying whether a particular word in a sentence is used metaphorically. Traditional approaches often rely on supervised learning models that implicitly encode semantic relationships based on metaphor theories. However, these methods often suffer from a lack of transparency in their decision-making processes, which undermines the reliability of their predictions. Recent research indicates that LLMs (large language models) exhibit significant potential in metaphor detection. Nevertheless, their reasoning capabilities are constrained by predefined knowledge graphs. To overcome these limitations, we propose DMD, a novel dual-perspective framework that harnesses both implicit and explicit applications of metaphor theories to guide LLMs in metaphor detection and adopts a self-judgment mechanism to validate the responses from the aforementioned forms of guidance. In comparison to previous methods, our framework offers more transparent reasoning processes and delivers more reliable predictions. Experimental results prove the effectiveness of DMD, demonstrating state-of-the-art performance across widely-used datasets. © 2025 IEEE.

关键词： dual-perspective large language models metaphor detection metaphor theories self-judgment

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LabelIT: A Multi-cloud Resource Label Unification Tool 30th

LabelIT: A Multi-cloud Resource Label Unification Tool

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30th International conference on Cooperative Information Systems, CoopIS 2024

作者： Mechouche, Jeremy Mokni, Marwa Ramusat, Yann Devoteam Research 1 Rue Galvani Massy91300 France

ISBN: (纸本)9783031813740

In cloud environments, labels are often defined by cloud architects to categorise and describe their resources, such as virtual machines, storage and network components. These labels play a crucial role in organising, managing and tracking resources, making it easier to identify and analyse them. However, these labels are often inconsistent, abbreviated, or misspelled, leading to potential mismanagement of cloud resources. In this demonstration, we present LabelIT Kubernetes-based tool designed to standardize resource labels for improved cloud resource management. The label unification tool system is built using NLP (natural language processing) based analysis methods that combine syntactic and semantic similarity processing for accurate unification. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cloud computing Cloud resource management Labels Multi-cloud natural language processing

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Can Large language model analyze financial statements well? 9

Can Large language model analyze financial statements well?

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Joint Workshop of the 9th Financial Technology and natural language processing, FinNLP 2025, the 6th Financial Narrative processing, FNP 2025, and the 1st Workshop on Large language Models for Finance and Legal, LLMFinLegal 2025, co-located with the 31st International conference on Computational Linguistics, COLING 2025

作者： Wang, Xinlin Brorsson, Mats Interdisciplinary Centre for Security Reliability and Trust University of Luxembourg 29 Av. John F. Kennedy KirchbergL-1855 Luxembourg

ISBN: (纸本)9798891762091

Since GPT-3.5’s release, large language models (LLMs) have made significant advancements, including in financial analysis. However, their effectiveness in financial calculations and predictions is still uncertain. This study examines LLMs’ ability to analyze financial reports, focusing on three questions: their accuracy in calculating financial ratios, the use of these metrics in DuPont analysis and the Z-score model for bankruptcy prediction, and their effectiveness in predicting financial indicators with limited knowledge. We used various methods, including zero-shot and few-shot learning, retrieval-augmented generation (RAG), and fine-tuning, in three advanced LLMs and compared their outputs to ground truth and expert predictions to assess their calculation and predictive abilities. The results highlight both the potential and limitations of LLMs in processing numerical data and performing complex financial analyses. © 2025 Association for Computational Linguistics.

关键词： Zero-shot learning

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Semantic Knowledge Enhanced and Global Pointer Optimized Method for Medical Nested Entity Recognition 13th

Semantic Knowledge Enhanced and Global Pointer Optimized Met...

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13th International conference on natural language processing and Chinese Computing

作者： Song, Yilin Kong, Fang Soochow Univ Lab Nat Language Proc Suzhou Peoples R China

ISBN: (纸本)9789819794300;9789819794317

In the medical field, unstructured medical text holds rich medical knowledge. Identifying medical entities in this text accurately is crucial for structured medical databases, knowledge graphs, and intelligent diagnostic systems. Medical text has unique features, making it hard for traditional NER methods to identify complex medical entities. In particular, the recognition of nested entities within medical text poses a significant challenge, as it requires systems to recognize and understand the complex hierarchical relationships between entities, placing higher demands on traditional entity recognition systems. To overcome the challenges of nested entity recognition in medical text, we propose a method that combines semantic knowledge enhancement and global pointer optimization. Initially, we incorporate semantic prior knowledge of entity categories, capturing the interplay between labels and text by integrating label relationships. This allows us to obtain candidate entity information enriched with integrated label details. Following this, we establish a classification module to evaluate and score these candidate entities along with their labels, enabling entity prediction. To address nested entities, we introduce a Efficient GlobalPointer module that computes the likelihood of each text span being a specific entity type, thus bolstering nested entity recognition. By merging the outputs from both modules, we arrive at the final predicted entities. Experimental results indicate that our method excels on two flat entity datasets, CMedQANER and CCKS2017, as well as on the nested entity dataset CMeEE. Compared to baseline models, our approach demonstrates notable performance enhancements.

关键词： Named Entity Recognition Nested Entity Recognition Medical Named Entity Recognition

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An Automated Grading System for Competition Theses Assisted by NLP Models 24

An Automated Grading System for Competition Theses Assisted ...

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8th International conference on Electronic Information Technology and Computer Engineering, EITCE 2024

作者： Xue, Sijuan Duan, Xu Yang, Manqi College of Information Engineering Hainan Vocational University of Science and Technology Hainan Haikou China

ISBN: (纸本)9798400710094

As the digital transformation of education continues to advance, the inefficiency and subjectivity of traditional manual scoring methods have become increasingly prominent. To address this issue, this study developed an automated scoring process. The process includes several key steps: reading text files, training latent Dirichlet allocation (LDA) topic models, matching texts with conditional sentence libraries, and performing natural language processing (NLP) analysis. The scoring method used in this study follows the standards proposed by advocates of text scoring conditions to comprehensively evaluate the completeness and accuracy of the text. The entire paper is comprehensively evaluated from four dimensions: completeness, content, summary quality, and writing ability. © 2024 Copyright held by the owner/author(s).

关键词： natural language processing systems

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Automatic Text Simplification for the Legal Domain in Brazilian Portuguese 34th

Automatic Text Simplification for the Legal Domain in Brazil...

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34th Brazilian conference on Intelligent Systems

作者： Pereira, Francielle Vasconcellos Frazao, Ana Moreira, Viviane P. UFRGS Porto Alegre Inst Informat Porto Alegre RS Brazil USP Sao Paulo Sao Paulo Brazil

ISBN: (纸本)9783031790379;9783031790386

Legal and juridical documents such as rulings, laws, agreements, and contracts contain domain-specific terms and jargon, long and complex sentences that may be difficult to understand for laypeople without domain expertise, reading issues, or with a low education level. The simplification of these documents has been a concern for several years, aiming to democratize access to justice. Courts are already adopting simpler language, especially in documents aimed at laypeople, such as warrants and notifications, to enhance inclusion and clarity. Automatic textual simplification, a subfield of natural language processing, seeks to make complex texts more accessible. This paper explores the task of automatic text simplification in Portuguese for the legal domain. The main challenge here is the lack of datasets containing complex sentences and their simplified versions. This work investigates how existing datasets, methods, and metrics used for text simplification perform applied to legal texts in Portuguese. We present qualitative and quantitative analyses using five models. The results show that GPT-based models have the best results, but fine-tuning with domain data is a viable open-source alternative.

关键词： Automatic Text Simplification Legal Texts natural language processing Plain language

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Advancing Gender Equality in Media: Tackling Stereotypes and Biases with AI 16th

Advancing Gender Equality in Media: Tackling Stereotypes and...

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16th International KES conference on Intelligent Decision Technologies

作者： Liu, Zhan Darbellay, Anne Balet, Nicole Glassey Vuille, Valerie Univ Appl Sci & Arts Western Switzerland Media Innovat Lab HES SO Valais Wallis Delemont Switzerland DecadreE Geneva Switzerland

ISBN: (纸本)9789819774210;9789819774197;9789819774180

This study presents an innovative approach to evaluating media representations of gender-based violence by integrating natural language processing (NLP) techniques with the advanced capabilities of GPT-4, an Artificial Intelligence (AI)based large language model. We developed a set of 27 expert-defined criteria to analyze a corpus of news articles, initially utilizing NLP methods for foundational text analysis. For more complex criteria, we employed GPT-4 and further enhanced its precision with fine-tuning. Our results indicate a significant increase in accuracy, achieving an overall 76% accuracy rate in content evaluation, which is 9% points higher than using NLP alone. This research introduces a novel media content analysis framework and paves the way for future enhancements in automated journalism assessment and ethical reporting.

关键词： Artificial intelligence Automated evaluation Gender-based violence GPT-4 Media content analysis natural language processing

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MLEE: Event Extraction as Multi-label Classification Task at Token Level 6th

MLEE: Event Extraction as Multi-label Classification Task a...

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6th IFIP TC 12 International conference on Intelligence Science, ICIS 2024

作者： Yang, Jinshun Huang, Shuangxi Huang, Mingfeng Tsinghua University Beijing China

ISBN: (纸本)9783031712524

Event Extraction is an important task in natural language understanding, which aims to identify event trigger of pre-defined event types and their arguments of specific roles, has attracted a lot of attention from industry and academia. The previous works failed to address some issues, including error propagation problem, overlap and nest problem, and high complexity of model. This work proposes a novel model MLEE, which models Event Extraction task as Multi-Label classification task at token level, and processes the extraction task in a joint paradigm, can help solving issues mentioned above. The experiment verifies our model’s effectiveness. empirical results on DuEE and FewFC shows that MLEE outperforms previous best model, pushing trigger extraction F1 to 85.03% (+4.45%), argument extraction F1 to 78.85% (+2.72%) on DuEE, pushing trigger extraction F1 to 76.69% (+1.63%), argument extraction F1 to 76.53% (+5.27%) on FewFC. © IFIP International Federation for Information processing 2025.

关键词： natural language processing systems

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