TY - GEN
T1 - Re-ranking summaries based on cross-document information extraction
AU - Ji, Heng
AU - Liu, Juan
AU - Favre, Benoit
AU - Gillick, Dan
AU - Hakkani-Tur, Dilek
N1 - Funding Information:
This work was supported by the U.S. Army Research Laboratory under Cooperative Agreement Number W911NF-09-2-0053, the U.S. NSF CAREER Award under Grant IIS-0953149, Google, Inc., DARPA GALE Program, CUNY Research Enhancement Program, PSC-CUNY Research Program, Faculty Publication Program and GRTI Program. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either
Funding Information:
This work was supported by the U.S. Army Research Laboratory under Cooperative Agreement Number W911NF-09-2-0053, the U.S. NSF CAREER Award under Grant IIS-0953149, Google, Inc., DARPA GALE Program, CUNY Research Enhancement Program, PSC-CUNY Research Program, Faculty Publication Program and GRTI Program. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on
PY - 2010
Y1 - 2010
N2 - This paper describes a novel approach of improving multi-document summarization based on cross-document information extraction (IE). We describe a method to automatically incorporate IE results into sentence ranking. Experiments have shown our integration methods can significantly improve a high-performing multi-document summarization system, according to the ROUGE-2 and ROUGE-SU4 metrics (7.38% relative improvement on ROUGE-2 recall), and the generated summaries are preferred by human subjects (0.78 higher TAC Content score and 0.11 higher Readability/Fluency score).
AB - This paper describes a novel approach of improving multi-document summarization based on cross-document information extraction (IE). We describe a method to automatically incorporate IE results into sentence ranking. Experiments have shown our integration methods can significantly improve a high-performing multi-document summarization system, according to the ROUGE-2 and ROUGE-SU4 metrics (7.38% relative improvement on ROUGE-2 recall), and the generated summaries are preferred by human subjects (0.78 higher TAC Content score and 0.11 higher Readability/Fluency score).
KW - Information Extraction
KW - Multi-document Summarization
UR - http://www.scopus.com/inward/record.url?scp=78650917554&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78650917554&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-17187-1_42
DO - 10.1007/978-3-642-17187-1_42
M3 - Conference contribution
AN - SCOPUS:78650917554
SN - 3642171869
SN - 9783642171864
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 432
EP - 442
BT - Information Retrieval Technology - 6th Asia Information Retrieval Societies Conference, AIRS 2010, Proceedings
PB - Springer
ER -