KAI HONG

Google Inc.
76 9th Ave, New York, NY 10011
Email: kaihong AT google DOT com

About Me

I am a software engineer at Google New York, working on Search. I obtained my Ph.D. degree from NLP group of University of Pennsylvania. Prior to that, I got my bachelor's degree from Tsinghua University, where I was a member of Yao Class.

Here is my CV and here is my Google scholar page.

Publications

Content Selection in Multi-document Summarization
Kai Hong
Ph.D. Dissertation, University of Pennsylvania, 2015 [pdf]

System Combination for Multi-document Summarization
Kai Hong, Mitchell Marcus, Ani Nenkova
In Proceedings of EMNLP, 2015 [pdf] [bib]

Lexical Use in Emotional Autobiographical Narratives with Schizophrenia and Healthy Controls
Kai Hong, Ani Nenkova, Mary E. March, Amber P. Parker, Ragini Verma, Christian G. Kohler
Psychiatry Research, 2015 [link]

Entity Ranking for Descriptive Queries
Kai Hong, Pengjun Pei, Ye-Yi Wang, Dilek Hakkani-Tur
In Proceedings of SLT, 2014 [pdf] [bib] [link]

A Repository of State of the Art and Competitive Baseline Summaries for Generic News Summarization
Kai Hong, John M. Conroy, Benoit Favre, Alex Kulesza, Hui Lin, Ani Nenkova
In Proceedings of LREC, 2014 [pdf] [bib] [resource]

Improving the Estimation of Word Importance for News Multi-Document Summarization
Kai Hong, Ani Nenkova
In Proceedings of EACL, 2014 [pdf] [bib]

Lexical Differences in Autobiographical Narratives from Schizophrenic Patients and Healthy Controls
Kai Hong, Christian G. Kohler, Mary E. March, Amber A. Parker, Ani Nenkova
In Proceedings of EMNLP-CoNLL, 2012 [pdf] [bib]

Feature engineering combined with machine learning and rule-based methods for structured information extraction from narrative clinical discharge summaries
Yan Xu, Kai Hong, Junichi Tsujii, Eric Chang
Journal of the American Medical Informatics (JAMIA), 2012 [pdf] [bib] [demo]

Resources

SumRepo04: A Repository of State of the Art and Competitive Baseline Summaries for Generic News Summarization on the DUC 2004 data (LREC 2014).

SumCombine: The basic summaries used in our system combination paper (EMNLP 2015).