Gan, Q., Zhu, M., Li, M., Liang, T., Cao, Y., & Zhou, B. (2014). Document visualization: an overview of current research. Wiley Interdisciplinary Reviews: Computational Statistics, 6(1), 19-36.
文件視覺化(document visualization)是一種資訊視覺化技術，將詞語、文句、文件或它們之間的關係等文字資訊轉換為視覺形式，使得使用者在面臨大量的文件時可以更好的了解文件、減輕他們的心理負荷。文件通常較缺乏結構(minimally structured)，但有豐富的特徵(attributes)和後設資料(metadata)，因此相較於文本視覺化(text visualization)，文件視覺化主要著重在文件以及其包含的特徵和後設資料上。以下是幾種可能的應用：(1) 詞語的頻次與分布； (2) 語意內容與重複 (semantic content and repetition)； (3) 區別文件集群的主題； (4) 文件的核心內容；(5) 文件間的相似性；(6) 文件間的連結；(7) 文件內容改變的過程；以及 (8) 社交媒體上的資訊擴散與其他模式以及做為改善文本搜尋的方式。
本研究以視覺化的對象(visualization objects)與任務對蒐集到的文件視覺化技術進行分析，視覺化的對象分為單一文件、文件集合、串流文本訊息以及檢索結果，以下對各種視覺化任務進行說明：
單一文件的視覺化目的在快速了解與吸收核心內容與文本特徵，著重在詞語、片語、語意關係和內容上，分為三種類型：
1.呈現詞語頻次、分布與語彙結構等語彙特徵的語彙為基礎 (Vocabulary-Based)視覺化：
重要的技術有Tag Clouds [6,7]與Wordle [8,9]，這類技術利用位置、顏色與大小等方式呈現單一文件中的詞語頻次，近來parallel tag clouds (PTC) [10]、 ManiWordle [11]、 context preserving dynamic word cloud[12]和visualization of internet discussion with extruded word clouds. [13]等許多研究以這類方法為基礎，並加以改善。其他屬於這類但原理不同的方法還有TextArc [14]和DocuBurst [16]。
2. 呈現實體與其間關係的語意結構視覺化：
Semantic Graphs [19]利用Penn Treebank產生的剖析樹(parse tree)，產生每一句子內的主語-動詞-受語，在解決代名詞指代(pronominal anaphors)問題，將各實體相連，產生語意圖(semantic graph)。
3.呈現文件內容(Document Content)的特性與關係為基礎的視覺化：
例如WordTree [23]以樹狀結構表現詞語的上下文脈絡，樹的根節點是使用者選取的詞語，每一個分支表示詞語在文件上的上下文，節點大小代表各詞語的頻次。Arc Diagrams [25] 以半圓形的弧連結重複的次序列，用來顯示內容上重複的複雜模式(complex patterns of repetition)。
文件集合的視覺化在於顯現文件群集上的主題、文件間的相似與差異以及內容在時間上的改變，相關技術可分為
1. 文件主題的視覺化：目的在發現特定的主題以及反應各個不同主題之間的關係，著名的研究案例有ThemeScapes [26]和 INSPIRE的 ThemeView以及 The Galaxy [27]，分別以地形圖和散佈圖表現文件在主題上的分布情形，地形圖上各「山脈」的高度表示主題的強度。TopicNets則以網路圖的節點與連線呈現文件間在主題上的關係。ThemeRiver [29]和Topic Island [30]著重在文件集合內主題在時間的變化，以ThemeRiver [29]來說，X軸表時間，Y軸上則以不同顏色的「河流」代表各主題，河流的寬度表現主題主題在相關文件上的強度。
2. 文件核心內容的視覺化：目的在提供整個文件集合的概觀，例如Document Cards [35]用來呈現大量的文件集合，每一張卡片上包含文件上重要的詞語和影像，而詞語是由文本探勘(text mining)技術由文件上的文本抽取出來，影像則從文件上抽取或然後加以組合。
3. 版本更動的視覺化：呈現各版本上的差異。例如：History Flow [36] 的設計是用來顯示維基百科上不同版本的文件內容更動情形以及相對應的作者；另外，也有許多針對軟體程式碼發展的視覺化。
4. 文件關係的視覺化：發現在不同文件上實體的連結，實體包括人、地點、日期和組織等等，提供這個功能的視覺化技術如Jigsaw [46] 。其他的技術，如ContexTour [47]和PivotPaths [49] 為視覺化被使用在論文集合的應用、FacetAtlas [48]則將Google Health文件上的病因、症狀、處方和診斷等實體相連，
5. 文件相似性的視覺化：其目的在將相似的文件置於彼此接近的位置，並且能遠離不相似的文件。過去常利用自組織映射圖 (self-organizing map, SOM) [50]，將高維度的資料映射到2維平面上呈現，並使得資料間複雜而非線性的關係能夠以距離方式表現，著名的例子有 Lin 的研究[52]和WEBSOM [53]。
在文本視覺化的應用方面，由於近年社交媒體的盛行，即時的串流文本處理的研究大為盛行，研究問題包括主題與詞語的統計分析與表現、主題相關事件的凸顯以及文本訊息本身的視覺化 [54]，Christian Rohrdantz [55]進行了串流文本資料的即時視覺化相關研究的回顧，Whisper [57] 的研究可以追蹤社交媒體上的資訊擴散過程。另一個應用是檢索結果的視覺化介面，早期的研究成果如TileBars [58]，Sparkler[59]可同時將多個查詢問句的結果以視覺化的方式呈現，RankSpiral [60] 的視覺化呈現重點在比較多個查詢問句或不同搜尋引擎的檢索結果。
各種技術所提供的取用方法、對文件的要求與主要特色可參考下表
關於主要特色說明如下：
1. 擴充性 (Extension)：此方法可適用於大量的文件集合。
2. 多功能性 (Versatility)：適用於多種的視覺化任務。
3. 互動性 (Interactivity)：提供使用者比較直覺的人機介面，讓使用者參與研究與發展過程。
4. 技術 (Techniques)
在文本處理上採用可擴充 (scalable)、高效能 (high-performance) 的演算法；採用協調多視圖 (Coordinated and Multiple Views)；即時處理技術。
本研究並且提出文件視覺化有待發展的兩個研究方向，一為文件視覺化的評鑑方法 [62]，另一為理論基礎 [63]。
This overview introduces fundamental concepts of and designs for document visualization, a number of representative methods in the field, and challenges as well as promising directions of future development.
Document visualization is a class of the information visualization techniques that transforms textual information such as words, sentences, documents, and their relationships into a visual form, enabling users to better understand textual documents and to lessen their mental workload when faced with a substantial quantity of available textual documents. [1]
And compared with text visualization that aims to visualize information on the text level, document visualization concentrates more on visualizing documents that include attributes and metadata except the core textual contents.
Document visualization has significant advantages over helping people to analyze and control big quantities of textual information in many cases. For example, we can intuitively get access to (1) word frequency or distribution; (2) semantic content and repetition; (3) the topic or topics that define document clusters; (4) the core content of document; (5) similarity among documents; (6) the connections among documents; (7) how content changes over time; and (8) information diffusion or other interesting patterns in social media, as well as improve text searches.
Generally ‘document’ is a textual record or physical form/representation of ‘information’.
The evolving notion of ‘document’ among Jonathan Priest, Otlet, Briet, Sch ¨ urmeyer, and the other documentalists increasingly emphasized whatever functioned as a document rather than traditional physical forms of documents. [2]
And with the development of digital technology, anything exists physically in a digital environment, such as a mail message or a technical report, could be considered as a document.
Documents are often minimally structured and may be rich with attributes and metadata, especially when concentrated in a specific application domain.
We may learn from the good practical guidelines to create an effective user interface for an interactive information visualization tool, as propounded by Ben Shneiderman who suggested in a form of mantra that an effective information visualization tool should follow the principle:
Overview first, zoom and filter, then details on demand. [4]
The mantra is accompanied by a task taxonomy for information visualizations that specifies seven
tasks at a high level of abstraction [4]:
• Overview. Gain an overview of the entire collection.
• Zoom. Zoom in on items of interest.
• Filter. Filter out uninteresting items.
• Details-on-demand. Select an item or group and get details when needed.
• Relate. View relationship among items.
• History. Keep a history of actions to support undo, replay, and progressive refinement.
• Extract. Allow extraction of sub-collections and of the query parameters.
We firstly divide document visualization methods into three main categories:
(1) single document visualization that has more emphasis on individual words and actual single document contents;
(2) document collection visualization that has more emphasis on large document collections, themes and concepts across collection, and how documents are relate to others;
(3) extended document visualization which often deals with comprehensive tasks, involves other attributes beyond the content of documents, and is always applied in specific field, such as social media and search.
In single document visualization, the goal is to quickly understand and absorb core content and text
features. The visualization focuses on words, phrases, semantic relations, and contents.
1. Vocabulary-Based Visualization
Vocabulary is the basic unit of a document. The visualization assists people in understanding words through visual representation of the document vocabulary features, such as word frequency, word distribution, and lexical structure, thereby providing a general idea of contents and features in a document.
Tag Clouds [6,7] and Wordle [8,9] are representative methods mainly visualizing word frequency. They are widely used in the news media and personal home pages. They provide layouts of raw tokens, colored, and sized by the corresponding word frequency within a single document. We may know the main research areas/content discussed in the text by the compact visual form of words.
Recently, some other methods have been proposed, extending the tag/word cloud, such as
parallel tag clouds (PTC), [10] ManiWordle, [11] context preserving dynamic word cloud,[12] visualization of internet discussion with extruded word clouds. [13]
Other examples: TextArc [14], DocuBurst [16].
2. Visualization Based on Semantic Structure
Visualization based on semantic
structure usually use entities and their relationships to
reveal the semantic content.
Semantic Graphs [19] is a visualization based on the
semantic representation of a document in the form of a
semantic graph. Firstly, it extracts subject–verb–object
for each sentence by the Penn Treebank parse tree.
Then, it links the triplets to their corresponding entity,
which needs to resolve pronominal anaphors as well
as to attach the associate WordNet synset. Thus, the
document is summarized with the semantic graph and
the list of extracted triplets.
3. Visualization Based on Document Content
Visualization based on document content is not only
to search for specific words but also to obtain the
characteristics and relations of the contents in the
document.
The WordTree visualization provides the
representation of both word frequency and context.
Size is used to represent frequency of the term or
phrase. The root of the tree is a user-selected word
or phrase, and the branches represent the contexts in
which the word or phrase is used in the document.
Users can click on a branch, choose a different search
term or re-center the tree. [23]
Martin Wattenberg’s Arc Diagrams [25] is a
visualization method that focuses on showing complex
patterns of repetition. It is suited to the analysis
of highly structured data like musical compositions
and less well-structured data like a web page.
Repeated subsequences are identified and connected
by semicircular arcs. Height of the arcs represents the
distance between the subsequences; and thickness of
the arcs represents the length of the subsequences.
Document Collection Visualization
Document collection visualization
usually intends to reveal the topic or topics that define
document clusters, the similarities and differences
among documents, and how contents change over
time.
1. Visualization of Document Themes
The main goal is to
discover one or more specific topics and to reflect the
relationships among various topics.
It may be used to
find hot disciplines, evolutions, and trends.
The methods, such as ThemeScapes, [26] INSPIRE’s
ThemeView, and The Galaxy, [27] all developed
by the Pacific Northwest National Laboratory, having
less emphasis on the time factor, focus more on
characteristics of the document themes at some
specific points.
ThemeView uses a 3D terrain
map display to represent different themes. The height
of a mountain represents the theme’s strength, and
the distance between two mountains represents the
similarity between the two themes. Keywords are
used to distinguish each mountain. [27]
The Galaxy visualization uses a
similar approach that themes are visualized as 2D clouds of document points-stars in a theme galaxy
(Figure 8(b)). [27]
There are other representations for visualizing
documents and topics as nodes in a node-link
graph. TopicNets is a web-based system for visual
and interactive analysis of large sets of documents
using statistical topic models. [28] The main view is
a document topic graph which can allow aggregate
nodes. The time dimension is represented as a separate
visualization, with documents placed chronologically
around a broken circle, and connected to related topic
nodes which are placed inside the circle.
The methods, such as ThemeRiver [29] and Topic
Island, [30] have greater emphasis on the time factor,
focusing more on visualizing thematic variations over
time within a collection of documents.
ThemeRiver
is in the form of axes, with the X-axis representing
time and the Y-axis representing different themes.
The ‘river’ flows from left to right through time,
changing width to portray changes in theme strength
of corresponding documents. Rivers of different
colors represent different themes, and the width of
river (i.e., narrow or wide) indicates the strength
(decreasing or increasing) of an individual topic in the
associated documents. [29]
2. Visualization of Document Core Content
Visualization of document core content mainly intends to give an overview of a collection of documents without reading them entirely.
Document Cards [35] visualizes large document collections, such as paper collections and news reports, which contain both texts and images to describe facts, methods, or stories. It represents the document’s key content as a mixture of images and important terms, similar to cards in a top trumps game. [35]
The pipeline for creating Document Cards is as follows: firstly, extract the text from the original document, and use a text mining approach to extract the key terms; then go to the phases of image extraction, including image processing and image packing; finally layout the extracted key terms and images to generate the corresponding document cards.
3. Visualization
of Changes over Different Versions
Visualization of changes over different versions is
used to visualize differences among multiple document
versions that are generated over time.
History Flow [36] is designed to show changes
between multiple document versions on Wikipedia. It
can visualize the process of content changes and the
corresponding authors who make the amendments. It
also reveals some complex patterns of cooperation and
confliction, such as vandalism and repair, anonymity
versus named authorship, negotiation, and content stability.
Software visualization [37–39] focuses on visualizing
the software development. SeeSoft, [40] Augur, [41] and Advizor [42] are visualizations for code documents.
Xia gives visual insight into version control activities,
like architectural and coding differences between
two software versions. [43] Beagle visualizes changes
among different released versions. [44] Spectrograph
shows the time and location where changes happen in
the system. [45]
4. Visualization
of Document Relationships
With gradual increases in document quantity, the
concepts and entities within documents become
larger and larger, making the analyst’s task of
evaluation and sense-making more difficult. Thus,
it is quite meaningful to visualize connections
among documents. The visualization focuses on the
correlation among documents, like the connections
among entities across different documents.
Jigsaw [46] is an interactive visualization for
document exploration and sense-making, and it
supports the analysis of relationships among
documents. It visually shows connections between
entities in the documents; where entities could be people, places, dates, organizations, and so on. It is
suitable to documents describing a set of observations
or facts, like news stories and case reports. It
provides multiple views and each view provides a
different perspective.
There are other methods for visualizing relations
among multiple facets. ContexTour [47] presents the
relations among conferences, authors, and topics
in paper collections. FacetAtlas [48] shows relations
among causes, symptoms, treatments, and diagnoses
in Google Health documents. PivotPaths [49] visually
explores relations of authors, keywords, and citations
in academic publications.
5. Visualization of Document Similarity
In many cases of document collection visualizations,
the goal is to place similar documents close to each
other and dissimilar ones far apart.
The self-organizing map (SOM) [50] is a nonlinear
projection method. It expresses complex, nonlinear
relationships between high dimensional data items
into simple geometric relationships on a 2D display.
When applying to information retrieval, it usually
uses map displays. [51] Different colored areas represent
different concepts in documents. Size of area indicates
its relative importance in collection. Neighboring
regions show commonalities in concepts. Dots
in regions can represent documents. Additional
information can be referred in Xia Lin’s map display [52] and WEBSOM. [53]
With the rise of social media (a textual medium),
text streams, such as Twitter posts, are being generated
in volumes that grow every day. A large body of
research has appeared in recent years. Those works
have different focuses and always involve multiple
targets, such as dealing with the statistical analysis
and presentation of topics or terms, focusing on
the emergence of topic events, [33] and visualizing the
text messages themselves. [54]
Christian Rohrdantz [55] provides an overview of real-time visualization of
streaming text data.
STREAMIT [56] presents a similar
visual representation of text streams which applies to
news documents.
Whisper [57] fulfills the requirement
for tracing information diffusion processes in social
media, in a real-time manner.
Search Visualization visualizes the results of
search operations. The relatively early approach is
TileBars [58] that intends to minimize time and effort
for deciding which documents to view in detail.
Susan Havre [59] introduces a graphical method called
Sparkler for visually presenting and exploring the
results of multiple queries simultaneously.
RankSpiral
addresses the problem of how to enable users to
visually explore and compare large sets of documents
that have been retrieved by different search engines or
queries. [60]
We have mainly considered the visualization
objects and tasks when classifying document
visualization methods. Our classification is considered
more acceptable than other classifications (e.g.,
representations: pixel-based, map-based, tree-based
graphs, node-link diagrams, circle graphs, etc.1),
since visualization is usually task dependent, and
users commonly begin with data and tasks. Actually,
each method may belong to different category even
under the same classification criteria; and we classify
each method according to its key visualization focus
(the visualization objects and tasks).
In Table 1, we summarize and compare those
methods mainly from four aspects to give readers a
brief view.
• Characteristics visualized. The characteristics of
a document visualized by the method, as word
frequency, semantic relations, content, changes,
or connections among documents.
• Principles satisfied. The design principles satisfied,
as noted in Document and Document
Design section, the seven tasks: 1) Overview; 2) Zoom; 3) Filter; 4) Details-on-demand; 5) Relate; 6) History; 7) Extract.
• Requirements for a document. Document types
suitable to the visualization method, i.e.,
whether the visualization method has special
requirements for a document, like document
content, structure, etc.
• Main features. Discuss the visualization method’s
features, especially the versatility and interactivity.
Despite this, document visualization shares the
same pipeline: get the data (a document or documents),
transform it into vectors, then run algorithms
based on the tasks of interest (i.e., similarity, search,
clustering) and generate the visualizations.
Document visualization techniques combine human
wisdom and computer graphics, allowing users
to efficiently and intuitively browse, explore, and
understand the increasing quantity of documents.
1. Extension: Existing methods can be extended
to suit for large-scale document collections.
2. Versatility: It is significant to design relatively
general visualization models for different tasks
within this field, since existing methods always have narrow scope of application due to its
pointed direction.
3. Interactivity: It is important to design a more
intuitive man–machine interface to improve user’s
experience of interaction. Also it is crucial to
find some interstices to allow users to participate
in researching and developing process, especially
the testing period.
4. Techniques:
• Algorithms. Develop and adopt scalable, high-performance
algorithms for text processing,
such as text summary and clustering.
• Parallel processing technology. With the
adoption and popularity of Coordinated and
Multiple Views (CMV), a visualization system
usually includes multi-views.
• Real-time processing technology.
1. Evaluation Many document visualization methods
or even information visualization methods
lack a quantitative measurement which can indicate
the overall quality, novelty, uncertainly, and
other evaluative metrics. More recently, there exist
more and more publications that reflect upon current practices in visualization evaluation. In
fact, the BELIV workshop was created as a venue
for researchers and practitioners to ‘explore novel
evaluation methods, and to structure the knowledge
on evaluation in information visualization
around a schema’. [62]
2. Theoretical Foundations The 2007 Dagstuhl
Workshop identified collaborative information
visualization with theory building as major
directions for future development. [63]
