Learning to Rank Salient Content for Query-focused Summarization Using a Shared Decoder Approach

Core Concepts

Integrating Learning-to-Rank (LTR) techniques into query-focused summarization systems, particularly using a shared decoder approach for both summarization and segment ranking, significantly improves the relevance and faithfulness of generated summaries, especially for broad queries.

Abstract

• Bibliographic Information: Sotudeh, S., & Goharian, N. (2024). Learning to Rank Salient Content for Query-focused Summarization. arXiv preprint arXiv:2411.00324.
• Research Objective: This paper investigates the integration of Learning-to-Rank (LTR) with Query-focused Summarization (QFS) to enhance summary relevance by prioritizing important content segments within long documents.
• Methodology: The researchers propose LTRSUM, a novel system that extends the Segment Encoding (SEGENC) summarizer by incorporating LTR principles. LTRSUM utilizes a shared decoder for both summarization and LTR tasks, enabling it to learn the ranking of gold segments based on their relevance to the query. The model is trained jointly on summarization and LTR tasks using cross-entropy and listwise cross-entropy softmax loss functions, respectively. Experiments are conducted on the QMSum and SQuALITY datasets, comparing LTRSUM against several state-of-the-art QFS models.
• Key Findings: LTRSUM outperforms existing QFS models on the QMSum benchmark across all automatic metrics (ROUGE and BERTSCORE) and achieves comparable performance on the SQuALITY benchmark. Human evaluations demonstrate that LTRSUM generates summaries with higher relevance and faithfulness scores compared to baseline models, without sacrificing fluency. Notably, LTRSUM excels in summarizing content for broad queries, effectively prioritizing and ranking relevant segments.
• Main Conclusions: Integrating LTR into QFS, particularly using a shared decoder approach, significantly enhances the ability to generate concise and relevant summaries, especially for broad queries. The model's capacity to learn segment importance contributes to its effectiveness in capturing and presenting the most pertinent information.
• Significance: This research contributes to the field of query-focused summarization by introducing a novel approach that leverages LTR techniques to improve the relevance and coherence of generated summaries, particularly for long documents. The shared decoder approach offers a computationally efficient way to incorporate ranking mechanisms into the summarization process.
• Limitations and Future Research: The study identifies challenges related to imbalanced labels and segment summarizer deficiencies, suggesting potential avenues for improvement. Future research could explore transfer learning from larger datasets to address label imbalance and investigate hybrid approaches that combine sentence-level saliency detection with segment ranking for enhanced summarization quality.

Stats

LTRSUM achieves a 5% improvement in relevance and 4.3% in faithfulness on the QMSum dataset.
LTRSUM shows a 2.8% improvement in relevance and 2.4% in faithfulness on the SQuALITY dataset.
The model contains 406 million parameters.
Training took two days on a single NVIDIA A6000 GPU.
The study found that in 48% of underperforming cases, the model struggled with imbalanced labels where the number of gold segments was significantly smaller than non-gold segments.
In 39% of underperforming cases, the model encountered difficulties extracting the most relevant information from identified gold segments, highlighting the need for improved sentential saliency detection within segments.

Quotes

"This study examines the potential of integrating Learning-to-Rank (LTR) with Query-focused Summarization (QFS) to enhance the summary relevance via content prioritization."
"Our proposed system outperforms across all automatic metrics (QMSum) and attains comparable performance in two metrics (SQuALITY) with lower training overhead compared to the SOTA."
"Human evaluations emphasize the efficacy of our method in terms of relevance and faithfulness of the generated summaries, without sacrificing fluency."

Key Insights Distilled From

Learning to Rank Salient Content for Query-focused Summarization

by Sajad Sotude... at arxiv.org 11-04-2024

https://arxiv.org/pdf/2411.00324.pdf

Deeper Inquiries

How might the integration of LTR with QFS be adapted for real-time summarization tasks, such as summarizing live news feeds or online discussions?

Adapting LTR-enhanced QFS for real-time summarization of dynamic content like news feeds or online discussions presents exciting opportunities and challenges:
Challenges:

Dynamically Evolving Content: Real-time scenarios involve a constant influx of new information, requiring the system to adapt its ranking and summarization on-the-fly. Traditional LTR models are often trained offline on static datasets.
Latency Constraints: Real-time applications demand rapid summarization. The computational overhead of segment encoding, ranking, and summary generation needs careful optimization.
Noise and Redundancy: Live feeds and discussions often contain noisy or redundant information. The system must effectively filter and prioritize truly salient content.
Potential Solutions:

Incremental Learning and Ranking: Employ online or incremental learning techniques to update the LTR model as new data arrives. This could involve updating segment representations and ranking models in real-time.
Efficient Segment Encoding: Explore lightweight segment encoding methods or leverage pre-computed representations to reduce latency. Techniques like dynamic sliding windows for segment creation could be beneficial.
Real-Time Relevance Feedback: Incorporate user feedback (e.g., clicks, likes, shares) as implicit relevance signals to dynamically adjust segment rankings and improve future summarization.
Summarization with Time Decay: Assign higher importance to recent segments in the ranking process, ensuring the summary reflects the most up-to-date information.
Example:
Imagine a system summarizing a live Twitter feed about a breaking news event. As new tweets arrive, the system could:

Segment and Encode: Group tweets into small, coherent segments and encode them using a pre-trained language model.
Incremental LTR: Update the LTR model with new segments, considering both textual features and real-time signals like retweet counts and user engagement.
Dynamic Summarization: Generate a concise summary, prioritizing the most important and recent segments identified by the LTR model.

Could the performance on specific queries be improved by incorporating a mechanism that identifies and weighs keywords within both the query and the source segments?

Yes, incorporating a keyword identification and weighting mechanism could significantly enhance performance on specific queries in LTR-enhanced QFS.
How it Works:

Keyword Extraction: Employ techniques like TF-IDF, KeyBERT, or even fine-tuned language models to extract salient keywords from both the query and individual source segments.
Keyword Weighting: Assign weights to keywords based on their perceived importance. This could involve:

Query-Specific Weighting: Keywords appearing in the query receive higher weights, reflecting their centrality to the information need.
Segment-Level TF-IDF: Keywords with high TF-IDF scores within a segment are considered more important for that segment.

Enhanced Segment Ranking: Incorporate keyword weights into the LTR model. For instance, calculate a similarity score between query keywords and segment keywords, giving higher ranks to segments with stronger keyword alignment.

Benefits:

Improved Relevance: By explicitly considering keyword matches, the system can better identify segments that directly address the specific information needs expressed in the query.
Fine-grained Content Selection: Keyword weighting allows for a more nuanced understanding of segment relevance, going beyond simple word overlap to capture semantic similarity.
Example:
Consider a query: "What were the economic impacts of the 2020 pandemic?". A keyword-aware system might:

Extract Keywords: Identify keywords like "economic", "impacts", "pandemic", "2020" from the query.
Weight Keywords: Assign higher weights to these keywords in the ranking process.
Rank Segments: Prioritize segments containing these keywords, ensuring the summary focuses on the economic consequences of the pandemic.

If we consider the process of summarization as a form of knowledge distillation, how can we ensure that the "distilled" summary retains the most crucial nuances and complexities of the original information?

Viewing summarization as knowledge distillation highlights the challenge of preserving crucial nuances and complexities while condensing information. Here's how we can strive for this:
Challenges:

Information Loss: Condensing inherently leads to some information loss. The challenge is minimizing the loss of critical details and relationships.
Oversimplification: Summaries risk oversimplifying complex topics, omitting important caveats, alternative perspectives, or supporting evidence.
Bias Amplification: The distillation process can inadvertently amplify existing biases in the source material, leading to skewed or incomplete representations.
Potential Solutions:

Multi-Aspect Coverage: Encourage the model to cover diverse aspects of the topic, even if it means slightly longer summaries. This can be achieved through:

Diversity-Promoting Objectives: Incorporate metrics like coverage diversity or semantic similarity into the loss function to reward summaries that encompass a wider range of information.
Multi-Task Learning: Train the model on related tasks like question answering or key phrase extraction alongside summarization. This can help the model learn richer representations and capture different facets of the information.

Preserving Uncertainty and Contradictions: Instead of presenting a single, definitive narrative, explore techniques to:

Highlight Uncertainties: Train the model to identify and explicitly mention areas of uncertainty or conflicting information in the source.
Generate Multiple Summaries: Explore generating multiple, diverse summaries that represent different perspectives or aspects of the topic.

Bias Mitigation: Actively address potential biases during data preparation, model training, and evaluation. This includes:

Diverse Training Data: Ensure the training data represents a wide range of perspectives and writing styles.
Bias-Aware Evaluation: Go beyond surface-level metrics and evaluate summaries for potential biases and fairness.
Example:
When summarizing a scientific article, the system could:

Identify Key Findings: Extract the main findings, but also highlight areas of ongoing debate or limitations of the study.
Present Multiple Perspectives: If the article discusses different theories or interpretations, the summary could briefly present each perspective.
Use Cautious Language: Instead of making absolute statements, the summary could use language that acknowledges uncertainty (e.g., "The study suggests...", "Further research is needed...").