Affiliation of Author(s):Beijing University of Posts and Telecommunications

Teaching and Research Group:Center for Intelligence Science and Technology

Journal:Proceedings of the 28th International Conference on Artificial Neural Networks, LNCS 11730

Key Words:Summarization, DPPs, Autoencoder

Abstract:This paper introduces a novel perspective on unlabeled data driven technology for extractive summarization. Because unsupervised autoencoders, combined with neural network language models, help to capture deep semantic features for sentence quality, we propose to integrate autoencoders with sampling method based on Determinantal point processes (DPPs) [1] to extract diverse sentences with high qualities, and generate brief summaries. The unique fusion of unsupervised autoencoders and DPPs sampling has never been adopted before.

Indexed by:Essay collection

First Author:Lei Li

All the Authors:Natalia Vanetik,Marina Litvak

Correspondence Author:Zuying Huang

Discipline:Engineering

First-Level Discipline:Computer science and technology *

Document Type:C

Page Number:293–299

Number of Words:2300

Translation or Not:no

Date of Publication:2019-09-17