Improving Automatic Summarization for Browsing Longform Spoken Dialog

Document Title

Improving Automatic Summarization for Browsing Longform Spoken Dialog

Document Information

• Subject Area: Data processing and natural language processing, with a particular focus on automatic summarization techniques for longform spoken dialog.
• Keywords: Longform dialog, automatic summarization, speech recognition, information retrieval, user interface design, machine learning, data processing, semantic analysis, text readability.

Research Background and Issues

• Problems or Challenges:

  • Longform spoken dialog content (e.g., podcasts, interviews) is becoming increasingly popular, but users face inefficiency in accessing information as listening is slower than reading.
  • Longform audio content is difficult to browse or navigate quickly, and existing summarization techniques face the following issues:
    • Errors in automatic speech recognition (ASR).
    • Summaries generated lack readability, accuracy, and sufficiency.
    • Longform content cannot be effectively processed or summarized in a single pass.

• Significance:

  • Spoken dialog content often addresses critical topics (e.g., health, politics, economics), making it important to improve user efficiency in consuming such content.
  • Automatic summarization can serve as a navigation tool to help users quickly locate information of interest, but the summaries must be trustworthy and readable.

• Research Motivation and Related Work:

  • While existing systems provide content browsing support through speech recognition and summarization, they perform poorly in addressing structural issues unique to spoken content (e.g., ambiguity, unclear references, grammatical errors).
  • Current hierarchical summarization techniques can handle longform text but often result in entity loss and fragmented time sequences.

Solution

• Methods or Solutions:

  • Proposed three training-agnostic post-processing techniques: semantic reordering, coreference resolution and grammatical correction, and fact-consistency guided decoding.
  • Integrated the improved summaries into a user interface, using visual information cues to assist navigation.

• Innovations:

  • Optimized the quality of hierarchical summarization models to meet the unique requirements of spoken content.
  • Simplified the hierarchical summarization process, enhancing system transparency and user trust.
  • Introduced specialized visual information cues, including summary quality, information gain, and overall information display ratio.

• Implementation Steps:

  • Post-Processing Techniques:
    1. Improving Semantic Coherence: Reordered input dialog using entailment clustering techniques to enhance contextual coherence.
    2. Enhancing Readability: Resolved pronoun ambiguity using coreference resolution techniques and applied language models for grammatical corrections.
    3. Reducing Factual Errors: Guided decoding of automatic summaries by generating multiple candidate summaries and iteratively filtering them to optimize accuracy.
  • User Interface Design:
    • Displayed hierarchical summaries on the left and detailed summaries with original audio content on the right.
    • Provided information cues (e.g., overall information ratio, summary quality, and information gain) to assist users in exploration and decision-making.

Research Outcomes

• Specific Results:

  • Overall summary quality of the system (measured by ROUGE-2) improved by 19%.
  • Human evaluations showed a 25% improvement in readability, a 10% improvement in accuracy, and a 17% improvement in sufficiency of the summaries.

• Advantages Compared to Existing Methods:

  • Compared to previous hierarchical summarization techniques, the improved system significantly enhanced the accuracy of summarizing complex spoken content.
  • Provided a more user-friendly interface, enabling users to access desired content more quickly and accurately.

• Experiments and Evaluation Results:

  • Automatic Evaluation: Summaries generated by the system showed significant improvements over baseline models in ROUGE-2 and other metrics.
  • Human Evaluation: Human assessments confirmed the improved quality of the summaries.
  • User Behavior Study: Users completed information retrieval tasks in nearly half the time using the improved system while maintaining high information accuracy.

• Limitations and Future Directions:

  • The current system does not handle audio features such as intonation, pauses, and sarcasm, which are critical for semantic understanding in spoken summarization.
  • The information cue components still present a learning curve for users; future work could optimize their design to lower the usage barrier.
  • The depth and efficiency of processing longform input content remain limited; future research could explore integrating longer-sequence language models (e.g., Longformer).
  • Extend the approach to more audio types, such as scenarios involving multi-party conversations or frequent interruptions.

This study demonstrates how automatic summarization technology can be combined with user navigation behavior to effectively improve the efficiency and experience of browsing longform spoken content, providing technical solutions and design insights for handling complex data.