104 citations · 145 across the 11 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…
Scoring Sentence Singletons and Pairs for Abstractive Summarization
Logan Lebanoff, Kaiqiang Song, Franck Dernoncourt +4
When writing a summary, humans tend to choose content from one or two sentences and merge them into a single summary sentence. However, the mechanisms behind the selection of one o…
Improving the Similarity Measure of Determinantal Point Processes for Extractive Multi-Document Summarization
Sangwoo Cho, Logan Lebanoff, Hassan Foroosh +1
The most important obstacles facing multi-document summarization include excessive redundancy in source descriptions and the looming shortage of training data. These obstacles prev…
Automatic Detection of Vague Words and Sentences in Privacy Policies
Logan Lebanoff, Fei Liu
Website privacy policies represent the single most important source of information for users to gauge how their personal data are collected, used and shared by companies. However,…
Adapting the Neural Encoder-Decoder Framework from Single to Multi-Document Summarization
Logan Lebanoff, Kaiqiang Song, Fei Liu
Generating a text abstract from a set of documents remains a challenging task. The neural encoder-decoder framework has recently been exploited to summarize single documents, but i…
Abstract Meaning Representation for Multi-Document Summarization
Kexin Liao, Logan Lebanoff, Fei Liu
Generating an abstract from a collection of documents is a desirable capability for many real-world applications. However, abstractive approaches to multi-document summarization ha…