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Found 7 papers, 3 papers with code
Faithfulness-Aware Decoding Strategies for Abstractive Summarization
We present a systematic study of the effect of generation techniques such as beam search and nucleus sampling on faithfulness in abstractive summarization.
FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations
Despite recent improvements in abstractive summarization, most current approaches generate summaries that are not factually consistent with the source document, severely restricting their trust and usage in real-world applications.
Evaluating the Tradeoff Between Abstractiveness and Factuality in Abstractive Summarization
Neural models for abstractive summarization tend to generate output that is fluent and well-formed but lacks semantic faithfulness, or factuality, with respect to the input documents.
Efficiently Summarizing Text and Graph Encodings of Multi-Document Clusters
We also show improvements in a transfer-only setup on the DUC-2004 dataset.
Transductive Learning for Abstractive News Summarization
This applies to scenarios such as a news publisher training a summarizer on dated news and summarizing incoming recent news.
Multi-Task Networks With Universe, Group, and Task Feature Learning
We present methods for multi-task learning that take advantage of natural groupings of related tasks.
