- The paper introduces BAYE SUM, a Bayesian framework that extracts relevant sentences for query-focused summarization using known relevant documents.
- It employs probabilistic language models and KL divergence to compare query and sentence models, achieving state-of-the-art metrics like MAP and MRR on TREC datasets.
- BAYE SUM demonstrates robust performance against noisy relevance judgments and extends effectively to multidocument summarization in complex IR tasks.
Bayesian Query-Focused Summarization
The paper by Hal Daume III and Daniel Marcu introduces B AYE S UM , an innovative approach to sentence extraction in the context of query-focused summarization. This work is significant in the domain of information retrieval (IR), particularly where there are multiple documents relevant to a single query. BAYE S UM utilizes a Bayesian statistical framework to model relevant sentences, mitigating the issue of limited information typical in short queries.
Model Overview
BAYE S UM leverages relevant documents as a means of reinforcing query terms, essentially performing a form of justified query expansion within the LLMing framework for IR. The model contrasts query models directly against sentence models, rather than against document models, setting it apart from standard ad-hoc IR tasks. Importantly, BAYE SUM relies on known relevant documents for the query at hand, a situation that is frequently encountered in structured IR tasks and web search applications.
Statistical Foundations
The paper details the underpinnings of BAYE S UM in the context of LLMing for information retrieval. Specifically, it operates by constructing probabilistic LLMs that treat words within documents and queries as discrete entities. Sentences are thereby evaluated for their relevance to a given query through these models, using KL divergence as a measure of similarity between query and sentence models.
Implementation and Results
BAYE SUM's practical efficacy was demonstrated through rigorous experimentation on established datasets from the Text REtrieval Conference (TREC). The model achieved state-of-the-art performance across several metrics including mean average precision (MAP), mean reciprocal rank (MRR), and precision at two (P@2). Notably, BAYE S UM surpassed traditional models employing LLMing approaches and relevance feedback methods.
Robustness and Implications
A salient feature of BAYE S UM is its robustness to noisy relevance judgments. The experiments indicated that even when the IR engine performance is suboptimal, BAYE S UM maintains superior summarization capabilities compared to traditional methods. This finding is indicative of its potential for deployment in real-world scenarios where perfect relevance judgment is unattainable.
Multidocument Summarization
The applicability of B AYE S UM extends beyond single-document cases into multidocument summarization, as demonstrated in the Multilingual Summarization Evaluation (MSE) and the Document Understanding Conference (DUC). The system was among the top contenders, evidencing its capacity to handle complex summarization tasks without requiring a user query.
Conclusion and Future Directions
BAYE S UM signifies a notable contribution to the field of query-focused summarization and information retrieval. Its Bayesian foundation allows for flexible integration of additional sources of evidence or modeling considerations, such as redundancy and user preferences. Future research could explore the relaxation of the bag-of-words assumption towards more intricate linguistic structures, potentially integrating dependency parsing or leveraging syntactic trees.
In summary, BAYE S UM represents a robust and theoretically justified approach to query-focused summarization, establishing a promising baseline for future explorations and expansions in the domain of IR and automatic summarization.