Perspectives - Interactive Document Clustering in the Discourse Analysis Tool Suite

Tim Fischer; Chris Biemann

cs.CLFeb 17, 2026

Perspectives - Interactive Document Clustering in the Discourse Analysis Tool Suite

Authors: Tim Fischer, Chris Biemann

Organizations: Language Technology Group, Department of Informatics, University of Hamburg, Germany

Abstract

This paper introduces Perspectives, an interactive extension of the Discourse Analysis Tool Suite designed to empower Digital Humanities (DH) scholars to explore and organize large, unstructured document collections. Perspectives implements a flexible, aspect-focused document clustering pipeline with human-in-the-loop refinement capabilities. We showcase how this process can be initially steered by defining analytical lenses through document rewriting prompts and instruction-based embeddings, and further aligned with user intent through tools for refining clusters and mechanisms for fine-tuning the embedding model. The demonstration highlights a typical workflow, illustrating how DH researchers can leverage Perspectives's interactive document map to uncover topics, sentiments, or other relevant categories, thereby gaining insights and preparing their data for subsequent in-depth analysis.

Figures & tables

Figure 1: Perspectives’ document map. Center: interactive scatter plot of documents colored by cluster. Hovering over a document previews its content. Left: settings & refinement operations, including the model fine-tuning operation. Right: statistics & information about selected documents. Top: toolbar with search & filtering. The UI is inspired by popular clustering interfaces such as Nomic Atlas, leveraging a familiar design to accelerate adoption.

Figure 2: The proposed interactive clustering pipeline. The initial clustering process is guided by providing rewriting and embedding instructions (green) to focus the document representations on user-defined aspects. The established core pipeline (orange) identifies clusters and builds various textual representations. Users can post-process the clustering (blue) through refinement operations (grey), triggering some steps of the pipeline.

	emotion	genre	product	stars	topic	frames	blurbs	stance	bias
text	45.10	27.20	36.72	47.54	71.17	59.78	73.84	51.19	47.20
+inst	50.60	27.50	55.76	58.86	71.24	65.00	73.25	48.21	48.51
keyphrases	49.15	34.85	54.64	46.34	65.70	60.34	76.73	61.63	50.76
+inst	49.51	34.90	60.64	49.68	65.40	58.31	76.66	63.14	50.69
summary	47.69	32.59	48.16	52.14	60.62	64.34	76.46	62.47	48.41
+inst	48.04	32.64	61.18	54.02	61.91	65.91	74.80	68.74	52.53

Table 1: Evaluation of document rewriting, instruction-based embeddings, and few-shot fine-tuning. Top: unsupervised ( zero-shot ) results. Bottom: few-shot results using the best configuration (e.g, text+inst for emotion & topic).

Appendix figures & tables3 assets

Supplementary material from the paper’s appendix.

Appendix

Dataset	Instruction
emotion	Identify the main emotion expressed in the given (summary of a \| keyphrases of a) song text
genre	Identify the main genre of the given (summary of a \| keyphrases of a) song text
stars	Identify the sentiment of the given (summary of an \| keyphrases of an) Amazon review
product	Identify the category of an Amazon product based on the review (summary \| keyphrases)
topic	Identify the topic or theme of the given (summary of a \| keyphrases of a) news article
frame	Identify the framing of the given (summary of a \| keyphrases of a) news article

Appendix

Table 2: Instructions provided to the embedding model. We adopted them to resemble those of the original authors.

Figure 3: Detailed evaluation with increasing number of labeled examples per class.

Dataset	Prompt
emotion-summary	Write a concise summary (maximum 5 sentences) that focuses on the emotional
	tone of the following song lyrics. Analyze the lyrics to determine the main
	emotion being conveyed and describe how it is expressed.
	Conclude with an emotion categorization:
emotion-keyphrases	Generate keyphrases (max 5 phrases) that describe the emotional tone of
	the following song lyrics. Focus on phrases that reflect the emotional tone,

Appendix

Table 3: Prompts used to rewrite the original documents. We use the same prompts for topics and frames.

Clustering

Clustering groups data points by shared structure, developing methods to discover, validate, and interpret groups across diverse data and constraints.

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Leveraging large language models (LLMs) to analyze complex documents -- such as academic papers, technical manuals, and financial reports -- has emerged as a mainstream and critical task in both research and industry. In practice, users must first filter relevant documents from large collections and then conduct in-depth analysis (e.g. question answering) over the selected subset, yet existing systems flatten documents into plain-text chunks, discarding the rich hierarchical structures (sections, tables, figures, equations) and degrading downstream performance. We present DocMaster, a hierarchical structure-aware document analysis system. DocMaster parses documents into hierarchical document trees preserving original layouts and constructs a structure-aware semantic index that enables accurate document filtering and in-depth analysis. We demonstrate DocMaster through an interactive web interface that enables users to upload document collections, construct tree-based and multi-view semantic indices, filter relevant documents via natural-language conditions, and perform follow-up question answering over the filtered results. The source code, data, and demo are available at https://doc-master.github.io/.

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