BiSET: Bi-directional Selective Encoding with Template for Abstractive Summarization

By Kai Wang, Xiaojun Quan, Rui Wang. 2019

60 citations recorded by the source corpus.

The success of neural summarization models stems from the meticulous encodings of source articles. To overcome the impediments of limited and sometimes noisy training data, one promising direction is to make better use of the available training data by applying filters during summarization. In this paper, we propose a novel Bi-directional Selective Encoding with Template (BiSET) model, which leverages template discovered from training data to softly select key information from each source article to guide its summarization process. Extensive experiments on a standard summarization dataset are conducted and the results show that the template-equipped BiSET model manages to improve the summarization performance significantly with a new state of the art.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Simple and Effective Retrieve-Edit-Rerank Text Generation (2020, methodology): “We compare our framework’s performance against those of NFR, Re3Sum, and BiSET, showing the effectiveness of post-generation ranking.”
Controlling the Amount of Verbatim Copying in Abstractive Summarization (2019, background): “They include lvt5k1sent (Nallapati et al. 2016), Multi-Task w/ Entailment (Pa- sunuru and Bansal 2018), SEASS (Zhou et al., 2017), DRGD (Li et al. 2017), EntailGen+QuesGen (Guo, Pasunuru, and Bansal 2018), PG Networks (See, Liu, and Manning 2017), Struct+2Way+Relation (Song, Zhao, and Liu 2018), R3S”
A Survey of Knowledge-enhanced Text Generation (2020, methodology): “In [18, 145], the actual saliency S∗ (T,Y ) is calculated by ROUGE, while the predicted saliency of the template for the input sentence S (T,X ) is calculated by a bi-linear network.”
Automatic Document Sketching: Generating Drafts from Analogous Texts (2021, methodology): “Most use a retrieval-based method to extract similar references from the training corpus as prototypes (Cao et al., 2018; Yang et al., 2019; Wang et al., 2019; Gao et al., 2019; Peng et al., 2019), and learn to separate salient information and latent template structure.”
Deep Learning for Text Style Transfer: A Survey (2020, methodology): “Apart from the additional loss designs, using the pretrained language model GPT-2 (Radford et al. 2019) can lead to improvement by at least 7 BLEU points with human references Wang et al. (2019).”
How well do you know your summarization datasets? (2021, unknown): “…et al., 2020), (2) M ATCH S UMM (Lewis et al., 2020), (3) R EFRESH (Narayan et al., 2018b) , (4) T WO S TAGE RL (Song et al., 2020), (5) N EUSUMM (Wang et al., 2019) , (6) B OT - TOM U P (Gehrmann et al., 2018) (Lewis et al., 2020) For XSum, we use the outputs of 9 different summarization…”