Incremental Extractive Opinion Summarization Using Cover Trees
(2401.08047)Abstract
Extractive opinion summarization involves automatically producing a summary of text about an entity (e.g., a product's reviews) by extracting representative sentences that capture prevalent opinions in the review set. Typically, in online marketplaces user reviews accumulate over time, and opinion summaries need to be updated periodically to provide customers with up-to-date information. In this work, we study the task of extractive opinion summarization in an incremental setting, where the underlying review set evolves over time. Many of the state-of-the-art extractive opinion summarization approaches are centrality-based, such as CentroidRank (Radev et al., 2004; Chowdhury et al., 2022). CentroidRank performs extractive summarization by selecting a subset of review sentences closest to the centroid in the representation space as the summary. However, these methods are not capable of operating efficiently in an incremental setting, where reviews arrive one at a time. In this paper, we present an efficient algorithm for accurately computing the CentroidRank summaries in an incremental setting. Our approach, CoverSumm, relies on indexing review representations in a cover tree and maintaining a reservoir of candidate summary review sentences. CoverSumm's efficacy is supported by a theoretical and empirical analysis of running time. Empirically, on a diverse collection of data (both real and synthetically created to illustrate scaling considerations), we demonstrate that CoverSumm is up to 36x faster than baseline methods, and capable of adapting to nuanced changes in data distribution. We also conduct human evaluations of the generated summaries and find that CoverSumm is capable of producing informative summaries consistent with the underlying review set.
Overview
-
CoverSumm is a novel approach to efficiently update extractive summarization for content that rapidly changes, such as product reviews.
-
The technique bypasses the need to reprocess the entire dataset by using cover trees to maintain a relevant subset of data.
-
CoverSumm significantly outperforms traditional methods in speed, reaching up to 25 times faster processing.
-
The algorithm not only enhances speed but also maintains high accuracy and fidelity in reflecting the dynamic nature of user-generated content.
-
Theoretical and empirical analyses validate CoverSumm's effectiveness, but suggest that finding a universally optimal summarization method is still a challenge.
Introduction to Incremental Extractive Summarization
Extractive opinion summarization is an essential tool for businesses and consumers alike. It helps in distilling large volumes of textual opinions into concise summaries. Standard extractive summarization methods face a challenge when addressing ever-evolving content such as product reviews, requiring periodic updating of summaries to reflect the latest customer feedback. The conventional processes are computationally demanding, as they reprocess all reviews whenever new data is input. This paper introduces a novel approach called CoverSumm, designed to enhance the efficiency of updateable or incremental extractive summarization, providing timely and accurate opinion summaries.
Extractive Summarization Techniques
Traditional extractive summarization systems employ a static procedure, summarizing a fixed set of reviews. But when it comes to systems relying on user-generated content, this static approach is not sufficient due to the constant influx of new data. The paper examines unsupervised extractive summarization methods, where sentences are scored based on their salience, and those with high scores form the summary. Prevailing methodologies have used graph-based objectives and lexical methods. However, the most recent advancements utilize centrality-based approaches for improved performance. The crux of such methods, particularly CentroidRank, is to measure the closeness of review sentences in the representation space and consider the nearest neighbors to the centroid as the summary.
The CoverSumm Algorithm
Addressing the limitations and inefficiencies of existing summarization techniques, CoverSumm is introduced. It leverages the data structure of cover trees, focusing on minimizing presumptions about the centroid of incoming review representations. Instead of recalculating the entire dataset for each addition, it smartly maintains a subset of past reviews, anticipating they will remain close to the centroid over time. This reservoir of candidates considerably limits computational effort. CoverSumm is demonstrated through empirical evidence to be significantly faster than prior techniques–showcasing an acceleration of up to 25 times. The algorithm proves to work well in real-time scenarios by judiciously updating summaries with high fidelity.
Theoretical and Empirical Analysis
CoverSumm's efficiency is rooted in theoretical proofs showing it identifies exact nearest neighbors to the centroid while keeping computational overhead low. The paper presents detailed propositions and proofs confirming the algorithm's accuracy in maintaining summaries representative of the evolving review set. Experiments conducted on diverse datasets, including synthetic and real-world examples, support CoverSumm's practicality. The authors conclude that while CoverSumm represents a significant leap forward in unsupervised extractive summarization, determining the ideal summarization paradigm across different domains remains an open question. This research invites future exploration into the development of nimble summary-updating techniques across varying sectors.
Create an account to read this summary for free: