most citedGated End-to-End Memory Networks

10 citations · 27 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL20198 cited

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…

cs.CL20194 cited

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…

cs.CL20192 cited

Guiding Extractive Summarization with Question-Answering Rewards

Kristjan Arumae, Fei Liu

Highlighting while reading is a natural behavior for people to track salient content of a document. It would be desirable to teach an extractive summarizer to do the same. However,…

cs.CL201610 cited

Gated End-to-End Memory Networks

Julien Perez, Fei Liu

Machine reading using differentiable reasoning models has recently shown remarkable progress. In this context, End-to-End trainable Memory Networks, MemN2N, have demonstrated promi…

cs.CL20163 cited

A Language-independent and Compositional Model for Personality Trait Recognition from Short Texts

Fei Liu, Julien Perez, Scott Nowson

Many methods have been used to recognize author personality traits from text, typically combining linguistic feature engineering with shallow learning models, e.g. linear regressio…