Logan Lebanoff

Improving the Similarity Measure of Determinantal Point Processes for Extractive Multi-Document Summarization

2019 · arXiv (Cornell University)

The most important obstacles facing multi-document summarization include excessive redundancy in source descriptions and the looming shortage of training data. These obstacles prevent encoder-decoder models from being used directly, but optimization-based methods such as determinantal …

Understanding Points of Correspondence between Sentences for Abstractive Summarization

2020 · arXiv (Cornell University)

Logan Lebanoff, John Muchovej, Franck Dernoncourt, Doo Soon Kim, Lidan Wang, Walter Chang, Fei Liu. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. 2020.

Abstract Meaning Representation for Multi-Document Summarization

2018 · arXiv (Cornell University)

Generating an abstract from a collection of documents is a desirable capability for many real-world applications. However, abstractive approaches to multi-document summarization have not been thoroughly investigated. This paper studies the feasibility of using Abstract …

Scoring Sentence Singletons and Pairs for Abstractive Summarization

2019

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 or multiple source sentences remain …

Adapting the Neural Encoder-Decoder Framework from Single to Multi-Document Summarization

2018

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 its success can in part be attributed to the …