A topical VAEGAN-IHMM approach for automatic story segmentation
Feature representations with rich topic information can greatly improve the performance of story segmentation tasks. VAEGAN offers distinct advantages in feature learning by combining variational autoencoder (VAE) and generative adversarial network (GAN), which not only captures intricate data repre...
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| Format: | Article |
| Language: | English |
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AIMS Press
2024-07-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2024289?viewType=HTML |
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| author | Jia Yu Huiling Peng Guoqiang Wang Nianfeng Shi |
| author_facet | Jia Yu Huiling Peng Guoqiang Wang Nianfeng Shi |
| author_sort | Jia Yu |
| collection | DOAJ |
| description | Feature representations with rich topic information can greatly improve the performance of story segmentation tasks. VAEGAN offers distinct advantages in feature learning by combining variational autoencoder (VAE) and generative adversarial network (GAN), which not only captures intricate data representations through VAE's probabilistic encoding and decoding mechanism but also enhances feature diversity and quality via GAN's adversarial training. To better learn topical domain representation, we used a topical classifier to supervise the training process of VAEGAN. Based on the learned feature, a segmentor splits the document into shorter ones with different topics. Hidden Markov model (HMM) is a popular approach for story segmentation, in which stories are viewed as instances of topics (hidden states). The number of states has to be set manually but it is often unknown in real scenarios. To solve this problem, we proposed an infinite HMM (IHMM) approach which utilized an HDP prior on transition matrices over countably infinite state spaces to automatically infer the state's number from the data. Given a running text, a Blocked Gibbis sampler labeled the states with topic classes. The position where the topic changes was a story boundary. Experimental results on the TDT2 corpus demonstrated that the proposed topical VAEGAN-IHMM approach was significantly better than the traditional HMM method in story segmentation tasks and achieved state-of-the-art performance. |
| format | Article |
| id | doaj-art-33e620b48815430991f415ce1142f7c0 |
| institution | Kabale University |
| issn | 1551-0018 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj-art-33e620b48815430991f415ce1142f7c02024-11-19T01:27:16ZengAIMS PressMathematical Biosciences and Engineering1551-00182024-07-012176608663010.3934/mbe.2024289A topical VAEGAN-IHMM approach for automatic story segmentationJia Yu 0Huiling Peng1Guoqiang Wang 2Nianfeng Shi 31. School of Computer and Information Engineering, Luoyang Institute of Science and Technology, China 2. Software Research Institute, Technological University of Shannon, Ireland1. School of Computer and Information Engineering, Luoyang Institute of Science and Technology, China1. School of Computer and Information Engineering, Luoyang Institute of Science and Technology, China1. School of Computer and Information Engineering, Luoyang Institute of Science and Technology, ChinaFeature representations with rich topic information can greatly improve the performance of story segmentation tasks. VAEGAN offers distinct advantages in feature learning by combining variational autoencoder (VAE) and generative adversarial network (GAN), which not only captures intricate data representations through VAE's probabilistic encoding and decoding mechanism but also enhances feature diversity and quality via GAN's adversarial training. To better learn topical domain representation, we used a topical classifier to supervise the training process of VAEGAN. Based on the learned feature, a segmentor splits the document into shorter ones with different topics. Hidden Markov model (HMM) is a popular approach for story segmentation, in which stories are viewed as instances of topics (hidden states). The number of states has to be set manually but it is often unknown in real scenarios. To solve this problem, we proposed an infinite HMM (IHMM) approach which utilized an HDP prior on transition matrices over countably infinite state spaces to automatically infer the state's number from the data. Given a running text, a Blocked Gibbis sampler labeled the states with topic classes. The position where the topic changes was a story boundary. Experimental results on the TDT2 corpus demonstrated that the proposed topical VAEGAN-IHMM approach was significantly better than the traditional HMM method in story segmentation tasks and achieved state-of-the-art performance.https://www.aimspress.com/article/doi/10.3934/mbe.2024289?viewType=HTMLgenerative adversarial networkvariational autoencoderhdphidden markov modelstory segmentation |
| spellingShingle | Jia Yu Huiling Peng Guoqiang Wang Nianfeng Shi A topical VAEGAN-IHMM approach for automatic story segmentation Mathematical Biosciences and Engineering generative adversarial network variational autoencoder hdp hidden markov model story segmentation |
| title | A topical VAEGAN-IHMM approach for automatic story segmentation |
| title_full | A topical VAEGAN-IHMM approach for automatic story segmentation |
| title_fullStr | A topical VAEGAN-IHMM approach for automatic story segmentation |
| title_full_unstemmed | A topical VAEGAN-IHMM approach for automatic story segmentation |
| title_short | A topical VAEGAN-IHMM approach for automatic story segmentation |
| title_sort | topical vaegan ihmm approach for automatic story segmentation |
| topic | generative adversarial network variational autoencoder hdp hidden markov model story segmentation |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2024289?viewType=HTML |
| work_keys_str_mv | AT jiayu atopicalvaeganihmmapproachforautomaticstorysegmentation AT huilingpeng atopicalvaeganihmmapproachforautomaticstorysegmentation AT guoqiangwang atopicalvaeganihmmapproachforautomaticstorysegmentation AT nianfengshi atopicalvaeganihmmapproachforautomaticstorysegmentation AT jiayu topicalvaeganihmmapproachforautomaticstorysegmentation AT huilingpeng topicalvaeganihmmapproachforautomaticstorysegmentation AT guoqiangwang topicalvaeganihmmapproachforautomaticstorysegmentation AT nianfengshi topicalvaeganihmmapproachforautomaticstorysegmentation |