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VivesDebate: A New Annotated Multilingual Corpus of Argumentation in a Debate Tournament ...
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VivesDebate: A New Annotated Multilingual Corpus of Argumentation in a Debate Tournament ...
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VivesDebate: A New Annotated Multilingual Corpus of Argumentation in a Debate Tournament ...
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A Corpus-Based Sentence Classifier for Entity–Relationship Modelling
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In: Electronics; Volume 11; Issue 6; Pages: 889 (2022)
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Text Mining from Free Unstructured Text: An Experiment of Time Series Retrieval for Volcano Monitoring
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In: Applied Sciences; Volume 12; Issue 7; Pages: 3503 (2022)
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Transformer-Based Abstractive Summarization for Reddit and Twitter: Single Posts vs. Comment Pools in Three Languages
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In: Future Internet; Volume 14; Issue 3; Pages: 69 (2022)
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Abstract:
Abstractive summarization is a technique that allows for extracting condensed meanings from long texts, with a variety of potential practical applications. Nonetheless, today’s abstractive summarization research is limited to testing the models on various types of data, which brings only marginal improvements and does not lead to massive practical employment of the method. In particular, abstractive summarization is not used for social media research, where it would be very useful for opinion and topic mining due to the complications that social media data create for other methods of textual analysis. Of all social media, Reddit is most frequently used for testing new neural models of text summarization on large-scale datasets in English, without further testing on real-world smaller-size data in various languages or from various other platforms. Moreover, for social media, summarizing pools of texts (one-author posts, comment threads, discussion cascades, etc.) may bring crucial results relevant for social studies, which have not yet been tested. However, the existing methods of abstractive summarization are not fine-tuned for social media data and have next-to-never been applied to data from platforms beyond Reddit, nor for comments or non-English user texts. We address these research gaps by fine-tuning the newest Transformer-based neural network models LongFormer and T5 and testing them against BART, and on real-world data from Reddit, with improvements of up to 2%. Then, we apply the best model (fine-tuned T5) to pools of comments from Reddit and assess the similarity of post and comment summarizations. Further, to overcome the 500-token limitation of T5 for analyzing social media pools that are usually bigger, we apply LongFormer Large and T5 Large to pools of tweets from a large-scale discussion on the Charlie Hebdo massacre in three languages and prove that pool summarizations may be used for detecting micro-shifts in agendas of networked discussions. Our results show, however, that additional learning is definitely needed for German and French, as the results for these languages are non-satisfactory, and more fine-tuning is needed even in English for Twitter data. Thus, we show that a ‘one-for-all’ neural-network summarization model is still impossible to reach, while fine-tuning for platform affordances works well. We also show that fine-tuned T5 works best for small-scale social media data, but LongFormer is helpful for larger-scale pool summarizations.
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Keyword:
abstractive summarization; deep learning models; natural language processing; opinion mining; pool summarization; Reddit; social networks; transformer models; Twitter
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URL: https://doi.org/10.3390/fi14030069
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Using Conceptual Recurrence and Consistency Metrics for Topic Segmentation in Debate
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In: Applied Sciences; Volume 12; Issue 6; Pages: 2952 (2022)
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Capability Language Processing (CLP): Classification and Ranking of Manufacturing Suppliers Based on Unstructured Capability Data
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StaResGRU-CNN with CMedLMs: a stacked residual GRU-CNN with pre-trained biomedical language models for predictive intelligence
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Machine Learning approaches for Topic and Sentiment Analysis in multilingual opinions and low-resource languages: From English to Guarani
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Dynamics of prescriptivism and lexical borrowings in Contemporary French
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CorpusExplorer ; Eine Software zur korpuspragmatischen Analyse
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| 15 |
Deceptive Opinions Detection Using New Proposed Arabic Semantic Features
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In: ISSN: 1877-0509 ; EISSN: 1877-0509 ; Procedia Computer Science ; https://hal.archives-ouvertes.fr/hal-03299022 ; Procedia Computer Science, Elsevier, 2021, 189, pp.29 - 36. ⟨10.1016/j.procs.2021.05.067⟩ (2021)
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An Approach Utilizing Linguistic Features for Fake News Detection
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In: IFIP Advances in Information and Communication Technology ; 17th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI) ; https://hal.inria.fr/hal-03287679 ; 17th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2021, Hersonissos, Crete, Greece. pp.646-658, ⟨10.1007/978-3-030-79150-6_51⟩ (2021)
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Sentiment Analysis of Arabic Documents
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In: Natural Language Processing for Global and Local Business ; https://hal.archives-ouvertes.fr/hal-03124729 ; Fatih Pinarbasi; M. Nurdan Taskiran. Natural Language Processing for Global and Local Business, pp.307-331, 2021, 9781799842408. ⟨10.4018/978-1-7998-4240-8.ch013⟩ ; https://www.igi-global.com/ (2021)
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The Role of Inferences in Opinion Mining : Applications to Chinese Social Media ; Le rôle des inférences pour la fouille d'opinion : applications aux réseaux sociaux en langue chinoise
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In: https://tel.archives-ouvertes.fr/tel-03469568 ; Linguistique. Institut National des Langues et Civilisations Orientales- INALCO PARIS - LANGUES O', 2021. Français. ⟨NNT : 2021INAL0016⟩ (2021)
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The Machine in the Garden of Meter and Rythm
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In: Plotting Poetry. On Mechanically-Enhanced Reading ; https://hal.telecom-paris.fr/hal-03255491 ; Bories, Anne-Sophie ; Purnelle, Gérald ; Marchal, Hugues. Plotting Poetry. On Mechanically-Enhanced Reading, Presses universitaires de Liège, 2021, 978-2-87562-280-8 (2021)
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On Multi-domain Sentence Level Sentiment Analysis for Roman Urdu ...
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