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6 results for “text simplification”
When text simplification is not enough: Could a graph-based visualization facilitate consumers' comprehension of dietary supplement information?
<p>Background: Dietary supplements are widely used. However, dietary supplements are not always safe. For example, an estimated 23,000 emergency room visits every year in the United States were attributed to adverse events related to dietary supplement use. With the rapid development of the Internet, consumers usually seek health information including dietary supplement information online. To help consumers access quality online dietary supplement information, we have identified trustworthy dietary supplement information sources and built an evidence-based knowledge base of dietary supplement information—the integrated DIetary Supplement Knowledge base (iDISK) that integrates and standardizes dietary supplement related information across these different sources. However, as information in iDISK was collected from scientific sources, the complex medical jargon is a barrier for consumers' comprehension. Objective: To assess how different approaches to simplify and represent dietary supplement information from iDISK will affect lay consumers' comprehension.</p> <p>Methods: Using a crowdsourcing platform, we recruited participants to read dietary supplement information in four different representations from iDISK: (1) original text, (2) syntactic and lexical text simplification, (3) manual text simplification, and (4) a graph-based visualization. We then assessed how the different simplification and representation strategies affected consumers' comprehension of dietary supplement information in terms of accuracy and response time to a set of comprehension questions.</p> <p>Results: With responses from 690 qualified participants, our experiments confirmed that the manual approach, as expected, had the best performance for both accuracy and response time to the comprehension questions, while the graph-based approach ranked the second outperforming other representations. In some cases, the graph-based representation outperformed the manual approach in terms of response time.</p> <p>Conclusions: A hybrid approach that combines text and graph-based representations might be needed to accommodate consumers' different information needs and information seeking behavior.</p>
Indonesia Text Simplification Dataset
<p>This is a dataset for Indonesian text simplification. Dataset input is provided by Liputan6 dataset (Koto et. al., 2020) and referenced by the id attribute as a document number, and the sentence_index attribute as the sentence position inside the corresponding document. The dataset consists of 95 sentences with four cases: relative clauses (30), appositions (14), conjoined clauses (in complex sentences; 30), and anaphora (26). Notice that these cases are not mutually exclusive. There are 33 sentences that don’t handle these cases.</p>
When text simplification is not enough: Could a graph-based visualization facilitate consumers’ comprehension of dietary supplement information?
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Text simplification in second language: process and product data
<p>This folder contains data on text simplification collected from an experimental study with second-language university students. We adopted a pre-test and post-test design, and randomly divided participants into experimental and control group. In the pre-test, participants were given an extract of a corporate report dealing with sustainability and were asked to revise it to make it easier to read for a lay customer. Subsequently, they took part in training. The experimental group received training on both plain language and sustainability, while the control group received training exclusively on the topic of sustainability. In the post-test session (2-3 days after the pre-test), all participants were assigned a second extract of a corporate report dealing with sustainability, and were asked again to make it easier to read for a lay customer by applying what they had learned from their respective training. This design allowed us to examine the impact of plain language training on text simplification (revision) tasks. The texts were in English while the participants were native speakers of other languages (mainly Dutch), so the text simplification took place in their second language.</p> <p>This project (PLanTra) has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 888918.</p> <p>Please see "readme" file for additional information.</p>
NapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching Summarization
<p>Accessing medical literature is difficult for laypeople as the content is written for specialists and contains medical jargon. Automated text simplification methods offer a potential means to address this issue. In this work, we propose a summarize-then-simplify two-stage strategy, which we call NapSS, identifying the relevant content to simplify while ensuring that the original narrative flow is preserved. In this approach, we first generate reference summaries via sentence matching between the original and the simplified abstracts. These summaries are then used to train an extractive summarizer, learning the most relevant content to be simplified. Then, to ensure the narrative consistency of the simplified text, we synthesize auxiliary narrative prompts combining key phrases derived from the syntactical analyses of the original text. Our model achieves results significantly better than the seq2seq baseline on an English medical corpus, yielding 3%~4% absolute improvements in terms of lexical similarity, and providing a further 1.1% improvement of SARI score when combined with the baseline. We also highlight shortcomings of existing evaluation methods, and introduce new metrics that take into account both lexical and high-level semantic similarity. A human evaluation conducted on a random sample of the test set further establishes the effectiveness of the proposed approach.</p>
Data for: Benchmarking the Sentence-Level Simplification of Dutch Municipal Text
<p><a href="https://github.com/Amsterdam-AI-Team/dutch-municipal-text-simplification/blob/master/complex-simple-sentences/complex-simple-v1-anonymized.csv">The corpus</a> consists of 1311 automatically aligned complex-simple sentence pairs.</p> <div> <h2>Original documents</h2> <a href="https://github.com/Amsterdam-AI-Team/dutch-municipal-text-simplification/tree/master/complex-simple-sentences#original-documents"></a></div> <p>For the creation of the dataset, we have used ~50 documents provided by the Communications Department of the City of Amsterdam. The documents have diverse sources and purposes (e.g. reports, citizen letters, newsletters, etc.) and cover a variety of topics (legal, medical, urban planning, etc.).</p> <p>The documents were reviewed by an expert and contain edits related to simplification, but also tone of voice, spelling corrections and other improvements. For the alignment of the sentence, we have used the original and final version of the documents.</p> <div> <h2>Alignment</h2> <a href="https://github.com/Amsterdam-AI-Team/dutch-municipal-text-simplification/tree/master/complex-simple-sentences#alignment"></a></div> <p>The alignment processed consists of 2 steps. For each document, create candidate complex-simple pairs by:</p> <ol> <li>aligning paragraphs (based on TF-IDF similarity)</li> <li>aligning sentences within the aligned paragraphs (based on TF-IDF similarity)</li> </ol> <div> <h2>Post-processeding</h2> <a href="https://github.com/Amsterdam-AI-Team/dutch-municipal-text-simplification/tree/master/complex-simple-sentences#post-processeding"></a></div> <p>After the initial alignment of sentences we:</p> <ul> <li>filter the candidate pairs where differences are only in capitalization or punctuation</li> <li>drop duplicates</li> <li>for every complex sentence where there are multiple possible simple versions, create a new entry by merging all simple versions (under the assumption that a complex sentence was simplified by splitting it into 2 or more simple sentences)</li> <li>for every complex sentence, preserve the simple version with the lowest Levenshtein edit-distance</li> </ul> <div> <h2>Anonymization</h2> <a href="https://github.com/Amsterdam-AI-Team/dutch-municipal-text-simplification/tree/master/complex-simple-sentences#anonymization"></a></div> <p>In order to publish the dataset, the following changes have been performed to the dataset:</p> <p>We have removed:</p> <ul> <li>names (including those of people with public functions such as the gemeentesecretaris) -> substituted with [NAME]</li> <li>organizations (with the exception of public organization such as Gemeente Amsterdam and GGD, RIVM) -> substituted with [ORGANIZATION]</li> <li>addresses (whenever they included an exact street and number) -> substituted with [ADDRESS]</li> <li>phone numbers -> substituted with [NUMBER]</li> <li>a handful of sentence posing privacy or information security risks, or containing otherwise sensitive information -> replaced by "xxx xxx xxx" for transparency</li> </ul> <p>Finally, the anonymized version of the dataset does not contain further information about the source documents (e.g. their names), however, a document ID has been added in order to provide context information about sentences stemming from the same document.</p> <div> <h2>Acknowledgements</h2> <a href="https://github.com/Amsterdam-AI-Team/dutch-municipal-text-simplification/tree/master/complex-simple-sentences#acknowledgements"></a></div> <p>This dataset was created by <a href="https://amsterdamintelligence.com/" rel="nofollow">Amsterdam Intelligence</a> for the City of Amsterdam.</p> <p>We owe a special thank you to the Communications Department of the City of Amsterdam for providing us with the original 48 documents. We also thank <a href="https://www.linkedin.com/in/daniel-v-a60905139/" rel="nofollow">Daniel Vlantis</a> for providing feedback during the dataset creation and for extensive experiments with it.</p> <div> <h2>License</h2> <a href="https://github.com/Amsterdam-AI-Team/dutch-municipal-text-simplification/tree/master/complex-simple-sentences#license"></a></div> <p>This data is licensed under the terms of the European Union Public License 1.2 (EUPL-1.2).</p>
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