- The paper introduces a hybrid approach that combines chronological ordering with topical cohesion to enhance the coherence of multidocument news summaries.
- It evaluates two algorithms—Majority Ordering and Chronological Ordering—and validates improvements using human judges on 25 summary evaluations per method.
- The study highlights the importance of treating sentence ordering as a discrete module to boost reader comprehension and overall summary quality.
Inferring Strategies for Sentence Ordering in Multidocument News Summarization
The paper "Inferring Strategies for Sentence Ordering in Multidocument News Summarization" by Regina Barzilay, Noemie Elhadad, and Kathleen R. McKeown addresses a significant yet underexplored issue in the field of text summarization—sentence ordering in multidocument summarization systems. Unlike single document summarization where the order of sentences in the original document can guide the summary's order, multidocument summarization demands a more sophisticated approach as sentences are drawn from multiple sources.
Methodology and Experimentation
The authors present a corpus-based methodology aimed at deriving a coherent ordering strategy for news summaries. The paper prioritizes the development of an ordering mechanism that integrates constraints from both chronological sequence and topical relatedness. The authors embark on this quest with two core questions: the impact of ordering on reader comprehension and the methodologies to infer an effective sentence ordering strategy.
To conduct their analysis, the authors constructed a corpus of data sets with multiple acceptable orderings of single texts, designed to capture the variability and constraints in sentence orderings. The complexity of deriving a comprehensive training dataset led them to develop a hybrid method that combines automatic and manual corpus analysis to establish commonality trends in orderings.
Proposed Solutions
Two primary algorithms were evaluated. The "Majority Ordering" (MO) algorithm establishes order based on consistent sentence patterns across input texts. However, its reliance on consistent ordering across documents limits its applicability when encountering source documents with varied organizational structures. The "Chronological Ordering" (CO) algorithm uses timestamps, approximating event chronology by the first publication time in news articles. Though this approach benefits event-based sequences, it falls short when texts describe states or when background information does not follow a temporal progression.
Recognizing these limitations, the authors propose an augmented algorithm combining CO with a strategy to maintain topical cohesion—an essential factor in textual coherence. By identifying and preserving blocks of topically related sentences, this augmented algorithm significantly enhances the quality of sentence ordering, as validated by human evaluation metrics showing a notable improvement over the standalone strategies.
Results and Implications
The experimental setup utilized judges to assess 25 summaries for each algorithm—MO, CO, and the augmented algorithm. This comprehensive evaluation demonstrated a clear improvement in summary quality with the augmented algorithm, evidenced by a significant increase in the number of summaries graded as "Good."
The findings underscore the complexity and non-triviality of ordering in multidocument summarization. Theoretical implications indicate that sentence ordering should be a discrete module within summary generation architectures, as erroneous sequencing can disrupt user comprehension and diminish the summary's effectiveness. Practically, the integration of coherence via topical relatedness offers an operational framework adaptable to dynamic and diverse input data typically encountered in real-world applications.
Future Directions
While the paper provides compelling insights into sentence ordering strategies, there remains an opportunity to expand this research into other summarization types and domains beyond news. Exploring adaptive algorithms that can intelligently toggle between ordering strategies based on input characteristics could yield further improvements. Additionally, integrating advanced temporal normalization and causality inference methods can enhance chronological ordering efficacy. As research advances in AI and NLP, such multidimensional approaches to sentence ordering will play an increasingly critical role in producing high-quality, coherent summaries.