Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Computing | en_US |
| dc.contributor.advisor | Cao, Jiannong (COMP) | en_US |
| dc.creator | Liu, Shuaiqi | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/12810 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Neural abstractive summarization for long documents | en_US |
| dcterms.abstract | Long documents, like academic literature, financial reports, and legal instruments, are important information sources. Nowadays, people can access massive long documents through the Internet. Reading through all their acquired documents and finding their desired content would be a heavy burden. The high-quality summaries can help people quickly grasp the key information from original documents. Automatic text summarization techniques can be employed to produce concise summaries for long documents. Abstractive summarization methods can approximate how humans write summaries by capturing input documents’ salient content and generating novel sentences as summaries. | en_US |
| dcterms.abstract | In this thesis, I study neural abstractive summarization for long documents. I aim to train neural network models to generate informative, fluent, and non-redundant summaries covering the multi-granularity, multi-document, and multimodal salient content in various long documents. Some new challenges arise in order to accomplish this objective: 1) the scarcity of available datasets, 2) identifying the multi-granularity salient information scattered in long inputs, 3) incorporating multi-document and multimodal content when generating summaries, 4) evaluating the quality of the generated summaries, 5) improving the efficiency of model training and inference. To tackle the above challenges, I built multiple large-scale datasets, novel summarization methods, and evaluation metrics, which are summarized below. | en_US |
| dcterms.abstract | First, I built multiple large-scale long document summarization datasets for academic literature, financial reports, and legal instruments, which can be the foundation of long document summarization research. Meanwhile, my datasets support extending long document summarization research from unimodal to multimodal, from summarizing a limited number of documents to a large number of documents. | en_US |
| dcterms.abstract | Second, I propose a series of techniques to identify the multi-granularity salient information scattered in long documents. This thesis introduces novel attention mechanisms, category-based content alignment method, and the multistage content selection schema for identifying and encoding phrase-level, sentence-level, and segment-level salient content. | en_US |
| dcterms.abstract | Besides, my research validates the importance of jointly considering multimodal or multi-document content when summarizing long documents. This thesis proposes multiple methods incorporating salient content from text and tables into summary generation. Besides, this thesis also proposes methods to summarize multiple categories of salient content from a large number of documents and generate structured summaries. | en_US |
| dcterms.abstract | To evaluate various summarization methods, my research not only employs commonly used automatic evaluation metrics but also proposes novel evaluation metrics. We also compare different models’ generated summaries by human evaluation. | en_US |
| dcterms.abstract | Last but not least, my research leverages various techniques to improve the efficiency of model training and inference. This thesis not only proposes efficient summarization models but also adopts some memory-efficient training methods. These techniques enable training large neural summarization models over long inputs on an of-the-shelf GPU. | en_US |
| dcterms.abstract | I hope this thesis can promote the long document summarization research. Although this thesis presents novel datasets, methods, and evaluation metrics for this topic, it still has many open problems. I list some future research directions at the end of this thesis. | en_US |
| dcterms.extent | xxi, 172 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2024 | en_US |
| dcterms.educationalLevel | Ph.D. | en_US |
| dcterms.educationalLevel | All Doctorate | en_US |
| dcterms.LCSH | Computational intelligence | en_US |
| dcterms.LCSH | Automatic abstracting | en_US |
| dcterms.LCSH | Text processing (Computer science) | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | open access | en_US |
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