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Found 5 papers, 3 papers with code
Controlling the Amount of Verbatim Copying in Abstractive Summarization
In this paper, we present a neural summarization model that, by learning from single human abstracts, can produce a broad spectrum of summaries ranging from purely extractive to highly generative ones.
Ranked #12 on
Text Summarization
on GigaWord
A New Approach to Overgenerating and Scoring Abstractive Summaries
We propose a new approach to generate multiple variants of the target summary with diverse content and varying lengths, then score and select admissible ones according to users' needs.
Ranked #10 on
Text Summarization
on GigaWord
Modeling Endorsement for Multi-Document Abstractive Summarization
In this paper, we model the cross-document endorsement effect and its utilization in multiple document summarization.
Knowledge-grounded Natural Language Recommendation Explanation
Explanations accompanied by a recommendation can assist users in understanding the decision made by recommendation systems, which in turn increases a user's confidence and trust in the system.
DelucionQA: Detecting Hallucinations in Domain-specific Question Answering
Hallucination is a well-known phenomenon in text generated by large language models (LLMs).
