Comprehensive Review of Opinion Summarization

The abundance of opinions on the web has kindled the study of opinion summarization over the last few years. People have introduced various techniques and paradigms to solving this special task. This survey attempts to systematically investigate the different techniques and approaches used in opinion summarization. We provide a multi-perspective classification of the approaches used and highlight some of the key weaknesses of these approaches. This survey also covers evaluation techniques and data sets used in studying the opinion summarization problem. Finally, we provide insights into some of the challenges that are left to be addressed as this will help set the trend for future research in this area.unpublishednot peer reviewe

Ocular-based automatic summarization of documents: is re-reading informative about the importance of a sentence?

Automatic document summarization (ADS) has been introduced as a viable solution for reducing the time and the effort needed to read the ever-increasing textual content that is disseminated. However, a successful universal ADS algorithm has not yet been developed. Also, despite progress in the field, many ADS techniques do not take into account the needs of different readers, providing a summary without internal consistency and the consequent need to re-read the original document. The present study was aimed at investigating the usefulness of using eye tracking for increasing the quality of ADS. The general idea was of that of finding ocular behavioural indicators that could be easily implemented in ADS algorithms. For instance, the time spent in re-reading a sentence might reflect the relative importance of that sentence, thus providing a hint for the selection of text contributing to the summary. We have tested this hypothesis by comparing metrics based on the analysis of eye movements of 30 readers with the highlights they made afterward. Results showed that the time spent reading a sentence was not significantly related to its subjective value, thus frustrating our attempt. Results also showed that the length of a sentence is an unavoidable confounding because longer sentences have both the highest probability of containing units of text judged as important, and receive more fixations and re-fixations

Large-Margin Determinantal Point Processes

Determinantal point processes (DPPs) offer a powerful approach to modeling diversity in many applications where the goal is to select a diverse subset. We study the problem of learning the parameters (the kernel matrix) of a DPP from labeled training data. We make two contributions. First, we show how to reparameterize a DPP's kernel matrix with multiple kernel functions, thus enhancing modeling flexibility. Second, we propose a novel parameter estimation technique based on the principle of large margin separation. In contrast to the state-of-the-art method of maximum likelihood estimation, our large-margin loss function explicitly models errors in selecting the target subsets, and it can be customized to trade off different types of errors (precision vs. recall). Extensive empirical studies validate our contributions, including applications on challenging document and video summarization, where flexibility in modeling the kernel matrix and balancing different errors is indispensable.Comment: 15 page