[Paper Review] Reader-Aware Multi-Document Summarization: An Enhanced Model and The First Dataset

Piji Li, Lidong Bing+1 more|arXiv (Cornell University)|Aug 3, 2017

Natural Language Processing Techniques3 references3 citations

TL;DR

This paper introduces the first dataset and an enhanced VAE-based model, RAVAESum, for reader-aware multi-document summarization (RA-MDS), which jointly models news articles and reader comments to improve summary relevance. By leveraging comment-driven salience estimation, the model achieves significant ROUGE score improvements over baseline MDS methods, demonstrating that reader feedback enhances summary quality.

ABSTRACT

We investigate the problem of reader-aware multi-document summarization (RA-MDS) and introduce a new dataset for this problem. To tackle RA-MDS, we extend a variational auto-encodes (VAEs) based MDS framework by jointly considering news documents and reader comments. To conduct evaluation for summarization performance, we prepare a new dataset. We describe the methods for data collection, aspect annotation, and summary writing as well as scrutinizing by experts. Experimental results show that reader comments can improve the summarization performance, which also demonstrates the usefulness of the proposed dataset. The annotated dataset for RA-MDS is available online.

Motivation & Objective

• To address the gap in multi-document summarization that ignores reader perspectives and interests reflected in user comments.
• To develop a neural framework that jointly models news documents and reader comments to improve salience estimation and summary relevance.
• To create a high-quality, expert-annotated dataset for RA-MDS, which is the first of its kind.
• To evaluate whether reader comments can enhance summarization performance beyond traditional MDS approaches.

Proposed method

• Extends the VAESum model using variational autoencoders (VAEs) to jointly encode news sentences and reader comment sentences into shared latent representations.
• Shares neural parameters between news and comment encoders to enable joint learning of salient content from both sources.
• Estimates sentence salience via a joint data reconstruction objective that reconstructs both news and comment inputs using shared latent variables.
• Uses a phrase-based compressive unified optimization framework to generate final summaries from high-salience sentences.
• Applies a comment weight estimation module to dynamically adjust the influence of comments based on their relevance to the topic.
• Employs a Gaussian-based VAE architecture to filter noise and capture complex semantic relationships in both formal news and informal comments.

Experimental results

Research questions

• RQ1 Can integrating reader comments into the summarization process improve the relevance and quality of multi-document summaries?
• RQ2 How does joint modeling of news and comments via shared latent representations affect salience estimation compared to modeling news alone?
• RQ3 To what extent do noisy, informal reader comments contribute to improved summarization performance despite their linguistic variability?
• RQ4 How does the proposed RAVAESum model compare to baseline MDS models like VAESum in terms of ROUGE scores and summary coherence?
• RQ5 What specific aspects or terms are enhanced in the summary when reader comments are incorporated, and how do they differ from those in comment-agnostic models?

Key findings

• The RAVAESum model, which incorporates reader comments, achieves a ROUGE-SU4 score of 0.196, significantly outperforming the comment-agnostic RAVAESum-noC model (0.189), with a p-value < 0.05.
• The inclusion of reader comments improved ROUGE-1 and ROUGE-2 scores by 0.006 and 0.009, respectively, indicating better coverage and n-gram overlap.
• The model successfully identified and elevated the salience of terms like 'Oculus' in the 'Sony Virtual Reality PS4' topic, which were frequently mentioned in reader comments but underrepresented in news articles.
• Top-10 salience terms from the RAVAESum (+C) model included 'oculus' and 'virtual reality', reflecting reader interests not fully captured by news-only models.
• Case studies showed that RAVAESum generated more comprehensive summaries by incorporating contextually relevant sentences about external influences (e.g., Oculus), which were absent in summaries from models ignoring comments.
• The proposed RA-MDS dataset, annotated by experts, is publicly available and represents the first benchmark for reader-aware summarization, enabling future research in this domain.