Beyond Text Generation: Supporting Writers with Continuous Automatic Text Summaries.

Title of the Paper

Beyond Text Generation: Supporting Writers with Continuous Automatic Text Summaries

Paper Information

• Domain: Human-Computer Interaction (HCI) and Natural Language Processing (NLP)
• Keywords: Text summarization, human-computer interaction, natural language processing, writing support, semantic zooming, AI tools, self-annotation, structural revision

Research Background and Problem Statement

• Identified Challenges:

  • Revision is a critical yet challenging aspect of writing, involving:
    1. Conflicts between time constraints and task completion.
    2. Difficulty in maintaining an overview of the structure as the text grows.
    3. Beginners often focus only on lexical-level edits rather than structural and scope-related changes.
  • Current HCI research on high-level revision is limited, primarily focusing on lexical corrections or error detection.

• Significance:

  • Writing is a complex cognitive activity that must address both the writer’s intent and the reader’s needs.
  • Automatically generated text summaries can help users reflect on, plan, and revise their texts, thereby improving writing quality.

• Motivation and Related Work:

  • Traditional writing strategies (e.g., reverse outlining) have been proven to support structured revision but have not yet been designed as interactive tools.
  • Current AI-based writing tools typically focus on text generation and local edits, with limited support for high-level content reflection and structural optimization.

Proposed Solution

• Proposed Solution:

  • Designed a writing editor that supports continuous automatic text summarization to help users plan, structure, and reflect on their writing process.
  • The editor uses paragraph-level text summaries displayed in a sidebar corresponding to the text. The summaries are updated in real-time and generated using various natural language processing techniques.

• Innovations:

  • Introduced a writing support tool that aids users in reflecting on their text through automated paragraph summaries rather than directly generating or editing text.
  • The tool incorporates cognitive strategies from reverse outlining to generate objective summaries from user text while preserving the user’s agency in making revisions.

• Implementation Steps and Key Technologies:

  • Summarization types are categorized into three:
    1. Central Sentence Summarization (extracting key sentences from paragraphs using the TextRank algorithm).
    2. Abstract Summarization (generating paragraph summaries using the T5 model).
    3. Keyword Extraction (extracting paragraph keywords using the RoBERTa model).
  • The user interface includes two windows: a conventional text editor and a sidebar displaying summary content. Each paragraph corresponds to a card, offering drag-and-drop, merge, and delete functionalities.
  • An auto-updating caching mechanism optimizes computational efficiency by recalculating only the paragraphs modified by the user.

Research Outcomes

• Specific Outcomes:

  • The tool helps users reflect on and revise their writing, including examining paragraph scope and structure, identifying redundant content, and improving information organization.
  • Two writing strategies adopted by users:
    1. Bottom-up text development: Starting with a blank editor and gradually building paragraphs.
    2. Top-down text development: Copying all article paragraphs first and then using summaries for structural adjustments.
  • Introduced a new interaction model: AI generates and displays parallel content to support user self-assessment rather than directly generating text.

• Advantages:

  • Compared to traditional writing support tools, this system not only provides instant summaries but also helps users gain a global perspective of their text through these summaries.
  • Users perceive text summaries as an external perspective, promoting reflection and self-adjustment.

• Experimental Results:

  • Users found the tool effective in simplifying the reading of long texts and supporting content selection and paragraph structure optimization.
  • Rouge analysis indicates that the abstract summaries generated by the T5 model in this study are of comparable quality to its performance on standard datasets.

• Limitations and Future Directions:

  • Limitations:
    • The current experimental design involves limited interaction time, posing challenges in exploring revision functionalities during the writing process.
    • The system does not flexibly combine manual and automated annotations to support more complex writing goals.
  • Future Directions:
    • Explore the integration of automated summaries with other non-textual annotations (e.g., diagrams or handwritten notes).
    • Evaluate the system’s long-term effectiveness in real-world user scenarios, such as handling research papers or other professional writing tasks.
    • Test the tool’s applicability for different text types, such as creative writing and expository writing.