Salesforce Com, Inc.

Haopeng Zheng

Yingbo Zhou

Kazuma Hashimoto

Wojciech Kryscinski

Semih Yavuz

Embodiments described herein provide document summarization systems and methods that utilize fine-tuning of pre-trained abstractive summarization models to produce summaries that more faithfully track the content of the documents. Such abstractive summarization models may be pre-trained using a corpus consisting of pairs of articles and associated summaries. For each article-summary pair, a pseudo label or control code is generated and represents a faithfulness of the summary with respect to the article. The pre-trained model is then fine-tuned based on the article-summary pairs and the corresponding control codes. The resulting fine-tuned models then provide improved faithfulness in document summarization tasks.

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San Francisco, CA, United States of America

Haopeng Zheng