Weakly Supervised Neural Framework for Opinion Summarization
This paper by Stefanos Angelidis and Mirella Lapata introduces a neural framework for opinion summarization, focusing on leveraging weak supervision signals, namely product domain labels and user-provided ratings. The aim is to extract salient opinions from online product reviews and compile extractive summaries across various domains. The approach integrates two key components: aspect extraction and sentiment prediction, both operating under minimal supervision.
Core Contributions and Methodology
The authors present several significant contributions in this work. First, the implementation of MATE (Multi-Seed Aspect Extractor) refines prior aspect discovery models by incorporating domain-specific seed words for more precise aspect extraction. This tailored setup minimizes the need for manual interpretation of fine-grained aspects traditionally seen in topic models like ABAE (Aspect-Based Autoencoder). MATE leverages unsupervised learning techniques, enriched by a multi-task framework that enables it to perform better than existing methods in identifying aspect-relevant words. Domains included cover a variety of products such as televisions, keyboards, and Bluetooth headsets.
Second, the sentiment prediction component utilizes MilNet, a model trained using Multiple Instance Learning (MIL) on document-level sentiment labels, which facilitates segment-level sentiment prediction. Here, MilNet outperforms other methods by accurately modeling sentiment orientation without needing exhaustive sentiment annotations. By applying this sentiment prediction model in conjunction with MATE’s aspect extraction, the authors achieve enhanced summarization results that surpass strong baseline performances.
The paper introduces a dataset, OpoSum, composed of Amazon reviews across six product domains. Human annotations provided aspect segment identifications, salience labels, and extractive summaries, serving as benchmark data for evaluating summarization models.
Numerical Results and Human Evaluation
Empirical results demonstrate that the neural framework delivers improvements over existing systems in all three evaluation tasks: aspect extraction, opinion salience retrieval, and summary generation. The paper reports substantial enhancements in aspect identification accuracy, with MATE+MT (multi-task enhanced form of MATE) achieving the highest micro-averaged F1 scores.
For opinion salience, the integration of sentiment polarity and aspect probabilities results in robust retrieval metrics. The framework’s ability to prioritize highly relevant and informative opinions is confirmed by both automatic measures such as ROUGE and human evaluation metrics. The large-scale user paper solidifies the model's performance, with crowdworkers preferring system-generated summaries over other competitive models across criterions such as informativeness and coherence.
Implications and Future Directions
The proposed weakly supervised approach significantly reduces the reliance on extensive annotated data, suggesting practical applicability for real-world opinion aggregating systems. It opens up possibilities for exploring multiple document summarization techniques tailored for subjective data analysis in areas such as market research and customer feedback.
The theoretical implications extend to the development of more generalized models that can autonomously adapt across diverse domains. The methodology exemplifies potential pathways in machine learning to simultaneously advance sentiment analysis and aspect extraction using limited labeled datasets.
The authors anticipate future work encompassing the combination of aspect and sentiment identification, potentially through integrated architectures that unify extractive and abstractive summarization methods. Moreover, extending the framework to encompass multilingual datasets would further broaden its application scope, providing insights into global consumer patterns and preferences.
This work represents a versatile addition to the domain of opinion summarization, harnessing the power of neural networks to judiciously extract and refine user opinions from vast text corpora, leading to concise and insightful summaries.