Automatic Debate Text Summarization in Online Debate Forum

Chowanda, Alan Darmasaputra; Sanyoto, Albert Richard; Suhartono, Derwin; Setiadi, Criscentia Jessica

doi:10.1016/j.procs.2017.10.003

Cited by 11 publications

(3 citation statements)

References 8 publications

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“…Given a condition of argumentation summarizer needs, an automatic summarizer for argumentation specifically for political debates was built by some researchers [34]. Not only for political debates, automatic summarizer for online debate forum was also conducted as well [35].…”

Section: A Argument Annotationmentioning

confidence: 99%

Argument annotation and analysis using deep learning with attention mechanism in Bahasa Indonesia

et al. 2020

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Argumentation mining is a research field which focuses on sentences in type of argumentation. Argumentative sentences are often used in daily communication and have important role in each decision or conclusion making process. The research objective is to do observation in deep learning utilization combined with attention mechanism for argument annotation and analysis. Argument annotation is argument component classification from certain discourse to several classes. Classes include major claim, claim, premise and non-argumentative. Argument analysis points to argumentation characteristics and validity which are arranged into one topic. One of the analysis is about how to assess whether an established argument is categorized as sufficient or not. Dataset used for argument annotation and analysis is 402 persuasive essays. This data is translated into Bahasa Indonesia (mother tongue of Indonesia) to give overview about how it works with specific language other than English. Several deep learning models such as CNN (Convolutional Neural Network), LSTM (Long Short-Term Memory), and GRU (Gated Recurrent Unit) are utilized for argument annotation and analysis while HAN (Hierarchical Attention Network) is utilized only for argument analysis. Attention mechanism is combined with the model as weighted access setter for a better performance. From the whole experiments, combination of deep learning and attention mechanism for argument annotation and analysis arrives in a better result compared with previous research.

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Section: A Argument Annotationmentioning

confidence: 99%

Argument annotation and analysis using deep learning with attention mechanism in Bahasa Indonesia

et al. 2020

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“…Batcha and Zaki (2010) and Anh et al (2014) also used SVD and NMF-based approaches in their unsupervised generic document summarisation model and furthermore they used maximal marginal relevance (MMR) matrix to remove the redundancy from the summary. A point-based summarisation generates the summary from online debate forum by abstractive summarisation approach where point represents verb and its syntactic arguments (Chowanda et al, 2017).…”

Section: Related Workmentioning

confidence: 99%

Fuzzy logic-based single document summarisation with improved sentence scoring technique

Patel

Chhinkaniwala

2018

IJKEDM

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Text summarisation is compressed or condensed version of any text document. Due to increasing use of digitisation, massive amount of information is available on internet. Text summarisation is an emerging alternative for users to find relative information in automated shortened versions. In this paper we propose single document summarisation technique using shallow features of sentence to generate summary. The weight of sentences is calculated by applying score of different words and sentence-based statistical features. Here, most salient sentences are selected based on weight of sentences and are put together to generate summary. This is modeled using fuzzy inference system. This approach utilises fuzzy inference and fuzzy measures to find most significant sentences. The result of our proposed method is compared with other methods using recall oriented understudy for Gisting evaluation (ROUGE-N) measures on document understanding conferences (DUC) 2002 dataset and results show that our proposed method outperforms a few baseline methods.

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“…The interactions between arguments can be complicated, for example, one argument may be paired with multiple other arguments, forming one-to-many relations. This task is essential for understanding the structure of dialogical argumentation and can also support other related tasks, such as argument generation (Hua et al, 2019a) and debate summarization (Chowanda et al, 2017). Due to the rich interaction of complex arguments, peer review and rebuttal are perfect resources for APE, and have also been exploited in other tasks (Hua et al, 2019b;Fromm et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Argument Pair Extraction with Mutual Guidance and Inter-sentence Relation Graph

Bao¹,

Liang²,

Sun³

et al. 2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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Argument pair extraction (APE) aims to extract interactive argument pairs from two passages of a discussion. Previous work studied this task in the context of peer review and rebuttal, and decomposed it into a sequence labeling task and a sentence relation classification task. However, despite the promising performance, such an approach obtains the argument pairs implicitly by the two decomposed tasks, lacking explicitly modeling of the argument-level interactions between argument pairs. In this paper, we tackle the APE task by a mutual guidance framework, which could utilize the information of an argument in one passage to guide the identification of arguments that can form pairs with it in another passage. In this manner, two passages can mutually guide each other in the process of APE. Furthermore, we propose an inter-sentence relation graph to effectively model the interrelations between two sentences and thus facilitates the extraction of argument pairs. Our proposed method can better represent the holistic argument-level semantics and thus explicitly capture the complex correlations between argument pairs. Experimental results show that our approach significantly outperforms the current state-of-the-art model.

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Automatic Debate Text Summarization in Online Debate Forum

Cited by 11 publications

References 8 publications

Argument annotation and analysis using deep learning with attention mechanism in Bahasa Indonesia

Argument annotation and analysis using deep learning with attention mechanism in Bahasa Indonesia

Fuzzy logic-based single document summarisation with improved sentence scoring technique

Argument Pair Extraction with Mutual Guidance and Inter-sentence Relation Graph

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