Unsupervised Sentence Embeddings for Answer Summarization in Non-factoid CQA

Ha, Thi-Thanh; Nguyen, Thanh-Chinh; Nguyen, Kiem-Hieu; Vu, Van-Chung; Nguyen, Kim-Anh

doi:10.13053/cys-22-3-3027

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…For unsupervised learning methods , LexRank [ 59 ] is a textual PageRank-like algorithm that selects the most salient sentences from a reference. Using embedding similarity for sorting, W2VLSTM [ 60 ] is an improvement based on LexRank. With the development of a deep neural generation network, Seq3 [ 4 ] is proposed to use the “long-short-long” pattern to automatically generate a summary.…”

Section: Methodsmentioning

confidence: 99%

Inter- and Intra-Modal Contrastive Hybrid Learning Framework for Multimodal Abstractive Summarization

Zhang²,

Wang

et al. 2022

Entropy

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Internet users are benefiting from technologies of abstractive summarization enabling them to view articles on the internet by reading article summaries only instead of an entire article. However, there are disadvantages to technologies for analyzing articles with texts and images due to the semantic gap between vision and language. These technologies focus more on aggregating features and neglect the heterogeneity of each modality. At the same time, the lack of consideration of intrinsic data properties within each modality and semantic information from cross-modal correlations result in the poor quality of learned representations. Therefore, we propose a novel Inter- and Intra-modal Contrastive Hybrid learning framework which learns to automatically align the multimodal information and maintains the semantic consistency of input/output flows. Moreover, ITCH can be taken as a component to make the model suitable for both supervised and unsupervised learning approaches. Experiments on two public datasets, MMS and MSMO, show that the ITCH performances are better than the current baselines.

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Section: Methodsmentioning

confidence: 99%

Inter- and Intra-Modal Contrastive Hybrid Learning Framework for Multimodal Abstractive Summarization

Zhang²,

Wang

et al. 2022

Entropy

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“…Many researchers have implemented zero-shot learning for image processing applications (Xie et al, 2019;Fu et al, 2018b;Liu et al, 2018;Xiong et al, 2016;Gavves et al, 2015) and only a few works used it for text processing (Artetxe & Schwenk, 2019;Zhang et al, 2019;Fu et al, 2018a;Yazdani & Henderson, 2015). This paper focuses on the implementation of zero-shot learning for text processing, especially for nonfactoid question answering and then summarizes the appropriate answers using the summarization techniques adapted in (Ha et al, 2018;Cao et al, 2017). This model could be incorporated into teaching and learning platforms such as Massive Open Online Courses (MOOCs).…”

Section: Introductionmentioning

confidence: 99%

A Novel Framework Using Zero Shot Learning Technique for a Non-Factoid Question Answering System

Devi

Sathick

Khan

et al. 2021

International Journal of Web-Based Learning and Teaching Technologies

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Non-Factoid Question Answering (QA) is the next generation of textual QA systems, which gives passage level summaries for a natural language query, posted by the user. The main issue lies in the appropriateness of the generated summary. This paper proposes a framework for non-factoid QA system, which has three main components: (i) A deep neural network classifier, which produces sentence vector considering word correlation and context. (ii) Zero shot classifier that uses a multi-channel Convolutional Neural Network (CNN), to extract knowledge from multiple sources in the knowledge accumulator. This output acts as a knowledge enhancer that strengthens the passage level summary. (iii) Summary generator that uses Maximal Marginal Relevance (MMR) algorithm, which computes similarity among the query related answer and the sentences from zero shot classifier. This model is applied on the datasets WikiPassageQA and ANTIQUE. The experimental analysis shows that this model gives comparatively better results for WikiPassageQA dataset.

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Advancements and Trends in Non-Factoid Question Answering: A Comprehensive Systematic Literature Review

Uwoghiren,

Oladipupo,

Oyelade

2024

2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG)

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Unsupervised Sentence Embeddings for Answer Summarization in Non-factoid CQA

Cited by 3 publications

References 8 publications

Inter- and Intra-Modal Contrastive Hybrid Learning Framework for Multimodal Abstractive Summarization

Inter- and Intra-Modal Contrastive Hybrid Learning Framework for Multimodal Abstractive Summarization

A Novel Framework Using Zero Shot Learning Technique for a Non-Factoid Question Answering System

Advancements and Trends in Non-Factoid Question Answering: A Comprehensive Systematic Literature Review

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