Adversarial Domain Adaptation Using Artificial Titles for Abstractive Title Generation
A common issue in training a deep learning, abstractive summarization model is lack of a large set of training summaries. This paper examines techniques for adapting from a labeled source domain to an unlabeled target domain in the context of an encoder-decoder model for text generation. In addition to adversarial domain adaptation (ADA), we introduce the use of artificial titles and sequential training to capture the grammatical style of the unlabeled target domain. Evaluation on adapting to/from news articles and Stack Exchange posts indicates that the use of these techniques can boost performance for both unsupervised adaptation as well as fine-tuning with limited target data.
Code (0)
등록된 구현이 없습니다.
Tasks
Abstractive Text SummarizationArticlesDecoderDomain AdaptationText GenerationSimilar Papers 제목 키워드 기반
LipKey: A Large-Scale News Dataset for Absent Keyphrases Generation and Abstractive Summarization
Summaries, keyphrases, and titles are different ways of concisely capturing the content of a document. While most previous work has released the datasets of keyphrases and summarization separately, in this work, we intro…
Abstractive Text SummarizationDocument SummarizationAdaptSum: Towards Low-Resource Domain Adaptation for Abstractive Summarization
State-of-the-art abstractive summarization models generally rely on extensive labeled data, which lowers their generalization ability on domains where such data are not available. In this paper, we present a study of dom…
Abstractive Text SummarizationDomain AdaptationDiscriminative Adversarial Search for Abstractive Summarization
We introduce a novel approach for sequence decoding, Discriminative Adversarial Search (DAS), which has the desirable properties of alleviating the effects of exposure bias without requiring external metrics. Inspired by…
Abstractive Text SummarizationDomain AdaptationLipKey: A Large-Scale News Dataset with Abstractive Keyphrases and Their Benefits for Summarization
Summaries, keyphrases, and titles are different ways of concisely capturing the content of a document. While most previous work has addressed them separately, in this work, we jointly use the three elements via multi-tas…
Document SummarizationA Pilot Study of Domain Adaptation Effect for Neural Abstractive Summarization
We study the problem of domain adaptation for neural abstractive summarization. We make initial efforts in investigating what information can be transferred to a new domain. Experimental results on news stories and opini…
Abstractive Text SummarizationArticlesDomain Adaptation