8 citations · 18 across the 6 of their papers we have counts for
11 papers
Modeling Endorsement for Multi-Document Abstractive Summarization
Logan Lebanoff, Bingqing Wang, Zhe Feng +1
A crucial difference between single- and multi-document summarization is how salient content manifests itself in the document(s). While such content may appear at the beginning of…
Learning to Fuse Sentences with Transformers for Summarization
Logan Lebanoff, Franck Dernoncourt, Doo Soon Kim +3
The ability to fuse sentences is highly attractive for summarization systems because it is an essential step to produce succinct abstracts. However, to date, summarizers can fail o…
A Cascade Approach to Neural Abstractive Summarization with Content Selection and Fusion
Logan Lebanoff, Franck Dernoncourt, Doo Soon Kim +2
We present an empirical study in favor of a cascade architecture to neural text summarization. Summarization practices vary widely but few other than news summarization can provide…
Understanding Points of Correspondence between Sentences for Abstractive Summarization
Logan Lebanoff, John Muchovej, Franck Dernoncourt +4
Fusing sentences containing disparate content is a remarkable human ability that helps create informative and succinct summaries. Such a simple task for humans has remained challen…
Joint Parsing and Generation for Abstractive Summarization
Kaiqiang Song, Logan Lebanoff, Qipeng Guo +5
Sentences produced by abstractive summarization systems can be ungrammatical and fail to preserve the original meanings, despite being locally fluent. In this paper we propose to r…
Analyzing Sentence Fusion in Abstractive Summarization
Logan Lebanoff, John Muchovej, Franck Dernoncourt +4
While recent work in abstractive summarization has resulted in higher scores in automatic metrics, there is little understanding on how these systems combine information taken from…