Paper Reading AI Learner

Informative and Controllable Opinion Summarization

2019-09-05 11:11:41
Reinald Kim Amplayo, Mirella Lapata

Abstract

Opinion summarization is the task of automatically generating summaries for a set of opinions about a specific target (e.g., a movie or a product). Since the number of input documents can be prohibitively large, neural network-based methods sacrifice end-to-end elegance and follow a two-stage approach where an extractive model first pre-selects a subset of salient opinions and an abstractive model creates the summary while conditioning on the extracted subset. However, the extractive stage leads to information loss and inflexible generation capability. In this paper we propose a summarization framework that eliminates the need to pre-select salient content. We view opinion summarization as an instance of multi-source transduction, and make use of all input documents by condensing them into multiple dense vectors which serve as input to an abstractive model. Beyond producing more informative summaries, we demonstrate that our approach allows to take user preferences into account based on a simple zero-shot customization technique. Experimental results show that our model improves the state of the art on the Rotten Tomatoes dataset by a wide margin and generates customized summaries effectively.

Abstract (translated)

URL

https://arxiv.org/abs/1909.02322

PDF

https://arxiv.org/pdf/1909.02322.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot