Jointly Learning Guidance Induction and Faithful Summary Generation via Conditional Variational Autoencoders (2022)

Abstract: Abstractive summarization can generate high quality results with the development of the neural network. However, generating factual consistency summaries is a challenging task for abstractive summarization. Recent studies extract the additional information with off-the-shelf tools from the source document as a clue to guide the summary generation, which shows effectiveness to improve the faithfulness. Unlike these work, we present a novel framework based on conditional variational autoencoders, which induces the guidance information and generates the summary equipment with the guidance synchronously. Experiments on XSUM and CNNDM dataset show that our approach can generate relevant and fluent summaries which is more faithful than the existing state-of-the-art approaches, according to multiple factual consistency metrics.

## introduction document summarization aims to produce the shorter version of a document while preserving salient information, which helps people out of the information explosion @xcite @xcite . compared with extractive summarization that retrieves essential sentences from the source document, abstractive summarization has no constraint on the words and phrases, which has attracted more attention. with the development of neural network and the large pre-trained language models, systems can generate summarizes with a high level fluency and coherence @xcite @xcite . generating faithful summaries is a challenging task for abstractive summarization @xcite et @xcite . previous studies have shown that the generated summaries distort or fabricate the facts of the source document, which also refers to the hallucination phenomenon @xcite . it statistics that most models produce 80% summaries with factual errors in xsum dataset @xcite which limits the usage of summarization system @xcite . recent studies provide different guidance information as input to enhance the factual consistency of the summary @xcite . generally, these models act a separate two-stage processing, the guidance extracting by off-the-shelf tools and summary generation conditioned on source document and guidance. typically, @xcite propose an extensible guided summarization framework gsum, which has achieved impressive results. it uses an oracle to select guidance during training and extracts the keywords by out-of-box tools @xcite at test time. then two transformers @xcite are used to encode the source document and guidance. however, the performances of separate twostage processing models are limited by the external tools which may suffer from domain mismatch. in fact, the experiments of gsum have shown that the performance would have a significant gain when the model uses an oracle to select guidance in testing, rather than the external tools. moreover, the inaccuracy of the guidance extraction leads to the unfaithfulness of the summary. in this paper, we present a novel framework which trains guidance induction and summary generation (gisg) jointly via conditional variational autoencoder. specifically, we use phrases as the information granularity of our guidance and we induce the keyphrases of the source document, which appear in the summary semantically. first, we extract all phrases from the source document by part-of-speech tagger as candidates and we use latent variables to indicate the keyphrases. then we learn to induce the latent variables and generate the summary jointly. our approach avoids the domain mismatch of the external tools while the guidance extraction is refined during training. then the faithful summaries are generated conditioned on the accurate guidance information. experiments on xsum @xcite ) and cnndm @xcite datasets show that our approach can generate relevant and fluent summaries which is more faithful to the source document than existing state-of-the-art approaches, according to multiple factual consistency metrics. ## conclusion in this paper, we propose to learn guidance induction and summary generation jointly via conditional variational autoencoders. we use phrases as the information granularity of our guidance and we induce the keyphrases of the source document. these summaries are generated conditioned on the source document and the keyphrases, ensuring the important information is consistent with the source document. the experiments show that our approach can generate more faithful summaries than the existing state-ofthe-art approaches, according to multiple factual consistency metrics.