acl acl2011 acl2011-80 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Oliver Schneider ; Alex Garnett
Abstract: We present ConsentCanvas, a system which structures and “texturizes” End-User License Agreement (EULA) documents to be more readable. The system aims to help users better understand the terms under which they are providing their informed consent. ConsentCanvas receives unstructured text documents as input and uses unsupervised natural language processing methods to embellish the source document using a linked stylesheet. Unlike similar usable security projects which employ summarization techniques, our system preserves the contents of the source document, minimizing the cognitive and legal burden for both the end user and the licensor. Our system does not require a corpus for training. 1
Reference: text
sentIndex sentText sentNum sentScore
1 com Abstract We present ConsentCanvas, a system which structures and “texturizes” End-User License Agreement (EULA) documents to be more readable. [sent-4, score-0.065]
2 The system aims to help users better understand the terms under which they are providing their informed consent. [sent-5, score-0.028]
3 ConsentCanvas receives unstructured text documents as input and uses unsupervised natural language processing methods to embellish the source document using a linked stylesheet. [sent-6, score-0.267]
4 Unlike similar usable security projects which employ summarization techniques, our system preserves the contents of the source document, minimizing the cognitive and legal burden for both the end user and the licensor. [sent-7, score-0.508]
5 1 Introduction Less than 2% of users read End-User License Agreement (EULA) documents when indicating their consent to the software installation process (Good et al. [sent-9, score-0.239]
6 While these documents often serve as a user’s sole direct interaction with the legal terms of the software, they are usually not read, as they are presented in such a way as is divorced from the use of the software itself (Friedman et al. [sent-11, score-0.242]
7 To address this, Kay and Terry (2010) developed what they call Textured Consent agreements which employ a linked stylesheet to augment salient parts of a EULA document. [sent-13, score-0.195]
8 We have developed a system, ConsentCanvas, for automating the creation of a Textured Consent document from an unstructured EULA based on the example XHTML/CSS template provided by Kay and Terry (2010; Figure 1). [sent-15, score-0.165]
9 Instead, it makes use of regular expressions and correlation functions to identify variable-length relevant phrases (Kim and Chan, 2004) to alter the document’s structure and appearance. [sent-17, score-0.313]
10 The system automates the labour intensive manual process used by Kay and Terry (2010). [sent-19, score-0.029]
11 We also present the first available implementation of Kim and Chan’s algorithm (2004). [sent-21, score-0.04]
12 As such, we contribute not just a working application, but also an extensible framework for the visual embellishment of plaintext documents. [sent-34, score-0.053]
13 1 Analysis Our system takes plain-text EULA documents as input through a simple command line interface. [sent-36, score-0.065]
14 It then passes this document to four independent submodules for analysis. [sent-37, score-0.129]
15 Each submodule stores the initial and final character positions of a string selected from within the document body, but does not modify the document before reaching the renderer step. [sent-38, score-0.354]
16 2 Variable-Length Phrase Finder The variable-length phrase finder module features a Python implementation of the Variable-Length Phrase Finding (VLPF) Algorithm by Kim and Chan (2004). [sent-40, score-0.283]
17 Kim and Chan’s algorithm was chosen for its domain independence and adaptability, as it can be fine-tuned to use different correlation functions. [sent-41, score-0.098]
18 42 This algorithm computes the conditional probability for the relative importance of variable-length ngram phrases from the source document alone. [sent-44, score-0.229]
19 It begins by considering every word a phrase with a length of one. [sent-45, score-0.043]
20 That is, every phrase of length m P{m} is considered as P{m-1 }w, where w is a following adjacent word. [sent-47, score-0.043]
21 Correlation is calculated between the leading phrase P{m-1 } and the trailing word w. [sent-48, score-0.091]
22 Phrases that maintain a high level of correlation are creating by appending the trailing word w, and those with a correlation score below a certain threshold are pruned before the next iteration. [sent-49, score-0.244]
23 This continues until no more phrases can be created. [sent-50, score-0.1]
24 The VLPF algorithm is able to use any of several existing correlation functions. [sent-52, score-0.098]
25 We have implemented the Piatetsky-Shapiro correlation function, the simplest of the three best-performing functions used by Kim and Chan, which achieved a correlation of 92. [sent-53, score-0.196]
26 We removed English stopwords, but did not perform any stemming when selecting relevant phrases because the selection of VLPs did not depend on global term co-occurrence, and we did not want to modify selected exact phrases. [sent-55, score-0.135]
27 We emphasize the top 15% meaningful phrases (as deter- mined by the algorithm) for the entire document. [sent-56, score-0.144]
28 15% was chosen for its comparable results to Kay and Terry’s example document (2010). [sent-57, score-0.129]
29 The phrase selected as the most relevant is also reproduced in the pull quote at the top of the document, as shown in Figure 3. [sent-58, score-0.252]
30 3 Contact Information Extractor The contact information extractor module uses regular expressions to match URLs, email addresses, or phone numbers within the document text. [sent-60, score-0.522]
31 4 Segmenter The segmenter module uses Hearst’s TextTiling algorithm to “segment text into multi-paragraph subtopic passages” (1997). [sent-63, score-0.234]
32 ConsentCanvas uses the NLTK implementation of the TextTiling algorithm. [sent-65, score-0.04]
33 Segmentation was not applied to the entire document (doing this resulted in a messy layout incoherent with structuring applied by headers and titles). [sent-66, score-0.204]
34 Instead, we used it to identify the lead paragraph of the document, which was rendered differently using the “lead paragraph” container in the template. [sent-67, score-0.067]
35 Future versions will use a more modern segmenting algorithm. [sent-68, score-0.03]
36 5 Header Extractor The header extractor module uses regular expressions to match any section header-like text from the original document. [sent-70, score-0.498]
37 Several different search strings were used to catch multiple potential header types, including but not limited to: • • • • 8 OR FEWER 3. [sent-71, score-0.201]
38 1Multi-level Eight or fewer ALL-CAPS TOKENS numbered headers numbered headers tokens separated by a line break Figure 3. [sent-73, score-0.238]
39 6 Rendering Each analysis submodule produces a list of character positions where found items begin and end. [sent-76, score-0.096]
40 These are passed to our rendering system, which inserts the corresponding HTML5 tags at the positions in original plaintext EULA. [sent-77, score-0.154]
41 We append a header to the output document to include the linked stylesheet per HTML5 specifications. [sent-78, score-0.433]
42 3 Analysis & Results We conducted a brief qualitative analysis on ConsentCanvas after implementation and debugging. [sent-79, score-0.067]
43 However, the problem space and system are not yet ready for formal verification or experimentation. [sent-80, score-0.032]
44 More exploration and refinement are required before we will be able to empirically determine if we have improved readability and comprehension. [sent-81, score-0.077]
45 1 Corpus We conducted our analysis on a small sample of EULAs from the same collection used by Lavesson et al. [sent-83, score-0.027]
46 In several of the best examples of texturized EULAs security concerns were highlighted; in the texturized version of one document, the pull quote was “on media, ICONIX, Inc. [sent-89, score-0.409]
47 warrants that such media is free from defects in materials and workmanship under normal use for a period of ninety (90) days from the date of purchase as evidenced by a copy of the receipt. [sent-90, score-0.093]
48 is free to use any ideas, concepts,” “(except one copy for backup purposes),” and “Inc. [sent-94, score-0.066]
49 ” Some phrases have incomplete words at the beginning and end; this is an artifact of a known but unfixed bug in the implementation, not a result of the algorithm. [sent-97, score-0.1]
50 Several short but frequent phrases were found to be VLPs, such as “Inc. [sent-99, score-0.1]
51 In short licenses consisting of only one to three paragraphs, sometimes no relevant VLPs were discovered. [sent-101, score-0.064]
52 There are also many phrases that should be highlighted that are not. [sent-102, score-0.132]
53 3 Preliminary System Evaluation We conducted an informal evaluation in which our system applied texture to 15 documents chosen from our corpus at random. [sent-104, score-0.192]
54 The pull quote text was nearly unintelligible in almost all cases, due largely to the fact that it did not split evenly on sentence borders. [sent-108, score-0.203]
55 We did not let this detract from our evaluation of the documents, because performance in this area was so consistently, and charmingly, poor, but did not affect readability of the main document body. [sent-109, score-0.235]
56 1 Comparisons with Kay and Terry Kay and Terry (2010) make reference to “augmenting and embellishing” the document text specifically not altering the original content. [sent-112, score-0.129]
57 However, their example document is written concisely in a user-friendly voice dissimilar to most formal EULAs found in the wild. [sent-113, score-0.161]
58 2 Handling Legal Language We had anticipated a considerable amount of difficult-to-understand legal language in the source document. [sent-116, score-0.148]
59 However, most documents were found to contain a number of high-frequency VLPs with both layperson-salient legal terminology and common clues to document structure. [sent-117, score-0.342]
60 The variable-length phrase finding module only incorporates a single correlation function. [sent-120, score-0.261]
61 Machine learning techniques might also be used to classify phrases as relevant or not, leading to better-emphasized content. [sent-122, score-0.135]
62 In the example license designed by Kay and Terry (2010), there are one or two emphasized phrases in each section. [sent-124, score-0.263]
63 The phrases found by ConsentCanvas are often sporadic, clustering in some sections and absent from others. [sent-125, score-0.1]
64 As a result of this, readability suffers, and so we may need to look into possible stratification of VLPs. [sent-126, score-0.077]
65 This might also aid multi-lingual documents, of which there are a few examples (a cursory look showed the results in French were comparable to those in culty in selecting meaningful phrases from diffi- English in a bilingual EULA in our corpus). [sent-127, score-0.173]
66 44 Contact information is currently emphasized in the same manner as salient phrases. [sent-128, score-0.085]
67 We plan to eventually embed hyperlinks for all URLs and email addresses found in the source document, as in Kay and Terry (2010). [sent-129, score-0.038]
68 The segmenter module uses the basic TextTiling algorithm with default parameters. [sent-130, score-0.186]
69 We plan to improve header extractor providing more sophisticated regular expressions; we found that a wide variety of header styles were used. [sent-133, score-0.539]
70 In particular, we plan to consider layouts that use digits, punctuation, or inconsistent capitalization in multiple instances in the document body. [sent-134, score-0.158]
71 There is currently no module that incorporates the “Warning” box from Kay and Terry (2010). [sent-135, score-0.12]
72 This module would be designed to select relevant multiline blocks of text by using techniques similar to the variable-length phrase finder or the segmenter. [sent-136, score-0.278]
73 This will enable customized texturing of EULAs and facilitate experimentation for understanding and evaluating gains in comprehension and readability. [sent-138, score-0.066]
74 Finally, we will conduct a formal user evaluation of ConsentCanvas. [sent-139, score-0.078]
75 5 Conclusion We have provided a description of the work in progress for ConsentCanvas, a system for automatically adding texture to EULAs to improve readability and comprehension. [sent-140, score-0.143]
76 45 Appendix The source code, our corpus, and a sample of converted documents are all available at: https://github. [sent-145, score-0.065]
77 Legal text summarization by exploration of the thematic structures and argumentative roles. [sent-149, score-0.056]
78 Stopping spyware at the gate: a user study of privacy, notice and spyware. [sent-163, score-0.112]
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