The Power of Summary-Source Alignments (2406.00842v1)

Abstract: Multi-document summarization (MDS) is a challenging task, often decomposed to subtasks of salience and redundancy detection, followed by text generation. In this context, alignment of corresponding sentences between a reference summary and its source documents has been leveraged to generate training data for some of the component tasks. Yet, this enabling alignment step has usually been applied heuristically on the sentence level on a limited number of subtasks. In this paper, we propose extending the summary-source alignment framework by (1) applying it at the more fine-grained proposition span level, (2) annotating alignment manually in a multi-document setup, and (3) revealing the great potential of summary-source alignments to yield several datasets for at least six different tasks. Specifically, for each of the tasks, we release a manually annotated test set that was derived automatically from the alignment annotation. We also release development and train sets in the same way, but from automatically derived alignments. Using the datasets, each task is demonstrated with baseline models and corresponding evaluation metrics to spur future research on this broad challenge.

• The paper introduces a novel proposition-level alignment framework that enhances salience detection, clustering, and fusion in multi-document summarization.
• It employs a hybrid approach with controlled crowdsourcing and the SuperPAL model to create the robust SPARK dataset for six summarization tasks.
• Experimental results demonstrate that fine-tuned models outperform GPT-3.5-turbo, underscoring the value of specialized training on detailed alignment data.

The Power of Summary-Source Alignments

The paper "The Power of Summary-Source Alignments" explores the utility of fine-grained proposition-level alignments between reference summaries and their source documents in a multi-document summarization (MDS) context. The authors present a methodology that extends traditional sentence-level alignment to more granular proposition spans, thereby enabling a broader range of summarization-related tasks with high precision.

Summary-Source Alignment Methodology

The core contribution of the paper is a comprehensive framework that leverages proposition-level alignments in MDS. The authors annotate alignment data manually using a controlled crowdsourcing method and further extend this dataset through automatic alignments using the SuperPAL model. The precise proposition-level alignment underpins the derivation of datasets for six distinct yet interrelated tasks within the summarization process:

1. Salience Detection: Identifying key information spans within a document set that are crucial for generating a coherent summary.
2. Proposition Clustering: Grouping semantically similar propositions across multiple documents.
3. Evidence Detection: Given a summary proposition, pinpointing all corroborating spans in the document set.
4. Sentence and Paragraph Planning: Structuring the information units in a logical and coherent sequence before generating the summary.
5. Sentence Fusion: Combining multiple document propositions into a single coherent summary sentence.
6. In-Context Fusion: Generating a whole passage by consolidating highlighted spans within their document context.

Dataset and Experimental Framework

The authors release a dataset called SPARK, meticulously annotated to support these six distinct tasks. The test set consists of densely annotated pairs of documents and summaries with proposition-level alignments, derived from Multi-News—an MDS dataset featuring news articles. Additionally, large-scale training and development sets are created using automated alignments, making the dataset robust and scalable.

Evaluation and Baseline Models

For each task, the authors provide baseline models and corresponding evaluation metrics:

• For Salience Detection, the Cross-Document LLM (CDLM) is adapted to assign global attention to candidate spans.
• Proposition Clustering and Evidence Detection utilize the SuperPAL model with adaptations for clustering and span extraction tasks.
• For Sentence and Paragraph Planning and Sentence Fusion, the Flan-T5-XXL model is fine-tuned with specific prompts and instructions.
• In-Context Fusion employs the QAMDen model, trained to process highlighted spans within a multi-document setup.

Results and Implications

The results indicate that finetuned models generally outperform GPT-3.5-turbo across all tasks, demonstrating that specialized training on task-specific datasets can yield notable improvements. However, GPT-3.5-turbo shows competitive performance in zero-shot and in-context settings, highlighting the potential for further fine-tuning and larger context adaptation.

Furthermore, the paper's annotation guidelines ensure high-quality data, as evident from the inter-annotator agreement score of 0.717. Analysis of the source dataset reveals that summary propositions often exhibit low lexical overlap with document propositions, indicating partial abstractiveness and low redundancy in the Multi-News dataset.

Future Directions

The paper opens pathways for advancing the summarization process through:

• Enhanced abstractive summarization by leveraging detailed proposition alignments.
• Improved fidelity in automatically derived datasets for various summarization-related tasks.
• Cross-domain applicability by fine-tuning alignment models like SuperPAL on diverse datasets beyond the news domain.

Future work can also explore refining models for better context understanding, leveraging incremental improvements in LLMs, and expanding the annotation framework to other languages and textual domains.

Conclusion

This paper underscores the potential of detailed summary-source alignments in refining the subtasks within MDS. By creating and critically evaluating a suite of datasets, the authors push the boundaries of how summarization tasks are approached. Their work highlights the nuanced interplay between fine-grained data annotations and task-specific model performance, suggesting that detailed alignments could be pivotal in future developments in AI-driven text summarization.