[Paper Review] Bayesian Query-Focused Summarization

Hal Daumé|ArXiv.org|Jul 10, 2009

Data Management and Algorithms8 references7 citations

TL;DR

BayeSum is a Bayesian query-focused summarization model that leverages multiple relevant documents to improve sentence extraction by modeling word sources (query-relevant, document-background, general English) and using probabilistic inference to rank sentences. It achieves state-of-the-art performance on DUC 2005 and MSE benchmarks, outperforming KL-based methods even with limited query information.

ABSTRACT

We present BayeSum (for ``Bayesian summarization''), a model for sentence extraction in query-focused summarization. BayeSum leverages the common case in which multiple documents are relevant to a single query. Using these documents as reinforcement for query terms, BayeSum is not afflicted by the paucity of information in short queries. We show that approximate inference in BayeSum is possible on large data sets and results in a state-of-the-art summarization system. Furthermore, we show how BayeSum can be understood as a justified query expansion technique in the language modeling for IR framework.

Motivation & Objective

• To address the challenge of low information content in short queries by leveraging multiple relevant documents as reinforcement for query terms.
• To develop a statistically principled method for query expansion within the language modeling framework for information retrieval.
• To create a query-focused summarization system that performs well even when relevance judgments are noisy or incomplete.
• To provide a flexible, parameter-free Bayesian framework that can be extended to incorporate additional knowledge sources or structural features.

Proposed method

• BayeSum models each word as generated from a mixture of three components: a general English language model, a document-specific background model, and query-specific language models for relevant queries.
• It uses a Bayesian hierarchical model with hidden variables to assign each word to one of the three sources, enabling probabilistic inference over sentence relevance.
• The system computes sentence relevance using KL divergence between the query model and sentence model, with smoothing against a background language model.
• Approximate inference is performed via variational Bayes or Gibbs sampling to scale to large datasets.
• The model integrates relevance judgments across multiple documents to refine query representations and improve sentence selection.
• It can be extended to include sentence compression and other linguistic features while maintaining coherence through the Bayesian framework.

Experimental results

Research questions

• RQ1 How can multiple relevant documents be leveraged to improve summarization performance for short or ambiguous queries?
• RQ2 Can a Bayesian framework provide a more robust and flexible alternative to traditional query expansion techniques in information retrieval?
• RQ3 To what extent does incorporating document-level relevance judgments improve sentence extraction accuracy in query-focused summarization?
• RQ4 How does the model's performance compare to existing state-of-the-art systems under standard evaluation metrics?

Key findings

• In the 2005 DUC competition, BayeSum achieved the highest responsiveness score among all systems, demonstrating superior query-focused summarization performance.
• On the DUC 2005 automatic Rouge evaluation, BayeSum ranked between third and sixth depending on the Rouge parameter settings, with no statistically significant difference from the top-performing systems.
• In the MSE 2005 evaluation, BayeSum achieved the highest human evaluation score of 0.529 on the pyramid metric, outperforming the next-best system by 0.040.
• BayeSum outperformed the KL-Rel model even when KL-Rel's parameters were tuned optimally, indicating superior flexibility from per-word, query-specific weighting.
• The system achieved a 95% confidence interval of [0.0429, 0.1057] on the automatic Basic Element metric, with a score of 0.0704, ranking third among 10 sites.
• Despite strong performance, the system scored poorly on linguistic quality in DUC 2005, likely due to rule-based sentence compression applied post-BayeSum.