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Found 11 papers, 8 papers with code
Comparative Opinion Summarization via Collaborative Decoding
Opinion summarization focuses on generating summaries that reflect popular subjective information expressed in multiple online reviews.
Aspect-Controllable Opinion Summarization
Recent work on opinion summarization produces general summaries based on a set of input reviews and the popularity of opinions expressed in them.
Convex Aggregation for Opinion Summarization
We found that text autoencoders tend to generate overly generic summaries from simply averaged latent vectors due to an unexpected $L_2$-norm shrinkage in the aggregated latent vectors, which we refer to as summary vector degeneration.
Ranked #1 on
Unsupervised Opinion Summarization
on Amazon
Unsupervised Opinion Summarization with Content Planning
The recent success of deep learning techniques for abstractive summarization is predicated on the availability of large-scale datasets.
Extractive Opinion Summarization in Quantized Transformer Spaces
We present the Quantized Transformer (QT), an unsupervised system for extractive opinion summarization.
Constructing Explainable Opinion Graphs from Review
The Web is a major resource of both factual and subjective information.
OpinionDigest: A Simple Framework for Opinion Summarization
The framework uses an Aspect-based Sentiment Analysis model to extract opinion phrases from reviews, and trains a Transformer model to reconstruct the original reviews from these extractions.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
Book QA: Stories of Challenges and Opportunities
We present a system for answering questions based on the full text of books (BookQA), which first selects book passages given a question at hand, and then uses a memory network to reason and predict an answer.
BookQA: Stories of Challenges and Opportunities
We present a system for answering questions based on the full text of books (BookQA), which first selects book passages given a question at hand, and then uses a memory network to reason and predict an answer.
Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised
We present a neural framework for opinion summarization from online product reviews which is knowledge-lean and only requires light supervision (e. g., in the form of product domain labels and user-provided ratings).
Multiple Instance Learning Networks for Fine-Grained Sentiment Analysis
We consider the task of fine-grained sentiment analysis from the perspective of multiple instance learning (MIL).
