Text Summary Augmentation for Intelligent Reading AssistantPramod Vadiraja

University of Kaiserslautern
Kaiserslautern, Germany
p_vadiraja18@cs.uni-kl.deAndreas Dengel

German Research Centre for
Artificial Intelligence (DFKI)
Kaiserslautern, Germany
Andreas.Dengel@dfki.de

Shoya Ishimaru

German Research Centre for
Artificial Intelligence (DFKI)
Kaiserslautern, Germany
Shoya.Ishimaru@dfki.de

ABSTRACTThis paper presents a technique to assist a reader. We aim to reduce manual efforts of the reader by leveraging the state-of-the-art
document summarization techniques and providing summaries
about unclear descriptions for each reader. Our system acts as aplug-and-play model that can be modified to support additional
methodologies. As a backend of the system, we investigated several
text summarization techniques and evaluated three techniques ofthem: TextRank, LexRank, and Luhn’s algorithm.

KEYWORDS
Cognitive augmentation, knowledge acquisition, human-documentinteraction, information retrieval, text summarization
ACM Reference Format:
Pramod Vadiraja, Andreas Dengel, and Shoya Ishimaru. 2021. Text Summary
Augmentation for Intelligent Reading Assistant. In Augmented HumansInternational Conference 2021 (AHs ’21), February 22–24, 2021, Rovaniemi,Finland. ACM, New York, NY, USA, 3 pages. https://doi.org/10.1145/3458709.
3459002

1INTRODUCTION

Attention can be described as a set of mechanisms that allows theallocation of cognitive resources, which are assumed to be limited [13]. There has been extensive scientific research that studies
the attention span of humans [15]. It is found to be very short-lived.
Hence it is important to protect the reader’s attention, to ensure animmersive cognitive experience while reading a document.
Consider a scenario where a person is reading academic material,different portions of the content will have an impact on cognitive
states at different levels. For instance, when a person is reading achapter on photosynthesis, the reader is able to give his/her undivided attention if he/she is aware of concepts such as carbohydrates,proteins, carbon-dioxide, and chlorophyll. If the reader is unawareof certain aspects of the chapter, the attention is divided and, insome cases, distracted, as this leads to a dip in interest or confidence.
This effect leads to a task-switching, which is an unconscious effortof the reader to switch attention from one task to another. Now,
the reader tries to read about the unclear concepts. In such cases,
due to the reader’s limited attention span, it becomes difficult for
the reader to maintain consistency while reading a material.
Permission to make digital or hard copies of part or all of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full citationon the first page. Copyrights for third-party components of this work must be honored.
For all other uses, contact the owner/author(s).
AHs ’21, February 22–24, 2021, Rovaniemi, Finland© 2021 Copyright held by the owner/author(s).
ACM ISBN 978-1-4503-8428-5/21/02.
https://doi.org/10.1145/3458709.3459002

Figure 1: An overview of the work-flow in a scenario when areader’s cognitive states fluctuate while reading a document.

Our text summary augmentation system supports both of areader and an editor. Although there are rational approaches ofpreparing a digital supporting material by an editor of the content [2, 5], it is not practical to delegate a lot of work to the experts.
We propose a solution to bring about an automated way of supportive content creation and decrease the efforts of experts.
The contributions of this paper are: (1) demonstrating the concept of our automatic text summary generation as a working systemand (2) reporting the results of a literature survey and an evaluationof text summarizations techniques.

2TECHNICAL BACKGROUND

Figure 1 shows an overview of the work-flow of our proposed
pipeline. It consists of the following two stages.
Information Construction – The cognitive states of a reader
are quantified by eye-tracking and physiological sensing. We utilize
an algorithm proposed by Jacob et al. for this step [6]. We extract
the regions in the document where the reader has relatively lower
confidence using the gaze locations. Then we process the extracted
textual content to remove stop-words, punctuations, etc., to filterout only the prominent keywords from these. The keyword set isutilized to query from a previously indexed data store. This cancorrespond to a database of digital copies of books that are indexedwith word entities using powerful indexing software like Apache
Lucene [10]. We can also get access to contents using Representational State Transfer (REST) APIs like Wikipedia API.
Summarization – With this kind of setup, if the reader is underconfident in some topics, he/she can just interact with the document,in particular with the words/topics of interest, which in-turn resultsin the summarised content with respect to the selected topic.
Ideally, the above steps should work on a real-time basis as thereader is involved in reading the document. But there might beproblems with respect to the latency of the API requests made.

AHs ’21, February 22–24, 2021, Rovaniemi, Finland

Pramod Vadiraja, Andreas Dengel, and Shoya Ishimaru
0.95

ROUGE-1

0.90
0.85
Luhn
TextRank
LexRank

0.80
0.75
2550

75

100125150
Summary length175200Figure 3: Text summarization performances
Figure 2: An example showing (a) a low confidence region,(b) extracted keywords (c), and summaries for keywords.

3THEORETICAL BACKGROUND

Our system utilizes a plug-and-play model that allows a developer
and a user to select text summarization techniques. There are several categories of summarization techniques [1]. We introduce themwith categorizing them into two types: extractive and abstractive.
Extractive summarization involves the task of extracting thecrucial or important sentences from a huge text, based on the similarity of the sentences with each other. There are several techniques
to perform extractive summarization: Neural Networks that classifies a sentence based on its importance, Text Rank based on thepage-rank algorithm [11], LexRank [4], and so on.
Abstractive summarization is the task of intelligently generating novel sentences by going through blocks of textual content.
Neural language models that have been pre-trained with hugetext corpuses like the Transformer [16] based BERT [3], and T5Transformer [12] are some of the recent developments in the areaof Natural Language Processing. These models can be trained fordownstream tasks like text summarization. Although an abstractive
summarization is currently an active area of research, there havebeen several works depicting promising results on human language
generations [8]. One advantage of abstractive summarization overthe extractive approach is that the abstractive approach can generate human-like sentences as a part of the summary. In contrast, theextractive approach generates few of the main sentences.

4DEMONSTRATION

We consider a scenario where a learner is reading a textbook in
Physics1 . As the first step, our system discover the regions of lower
confidence through analyzing the cognitive states of the reader
as shown in Figure 2 (a). We then preprocess the extracted textual content by removing stop-words, punctuations, performing
lemmatization, and filtering out the nouns chunks from the text
to improve our chances of extracting keywords with associatedconcepts as seen in Figure 2 (b). Even after filtering, there are stillsome keywords that were not filtered out. We use Wikipedia’s API
to extract text with respect to the queries from the previous step.
The information from the above step is passed to the TextRank
algorithm with minor changes in an attempt to improve results,
which can be seen in Figure 2 (c).
1 An online demo: https://shoya.io/projects/hypermind#ahs2021demo

5EVALUATIONSince we expect our system to function in near real-time, we utilized
the score of Recall-Oriented Understudy for Gisting Evaluation(ROUGE-1) [7] to evaluate some of the summarization techniques.
Essentially ROUGE-1 tells us the number of token overlappings
between the generated summary and a reference summary. For thisexperiment, we compared the three well-known summarizationtechniques: TextRank, LexRank, and Luhn’s algorithm [9].
Summaries are a concise representation that captures the coreessence/details of the main content. Hence it is important to be able
to determine the optimal length for summaries. The length could
be the number of tokens or the number of sentences in summary.
Therefore we considered the length as the number of sentencesin summary and generated summaries for a range of lengths. It isexpected that as the summary length increases, the chances of thesummaries capturing the core details increases as well.
Experimental Design – We used Wikipedia Summary dataset [14]that consisted of a mapping between topics and their respective
summaries. We fetched a set of Physics-related terms by crawling
the physics glossary page of Wikipedia. We considered 50 suchterms and used the Wikipedia API to fetch the respective contents.
These pages were preprocessed by performing tokenization andpunctuations removal. The extracted pages were summarized by
the three techniques, and their ROUGE-1 scores were measured. We
considered the following two measures: (1) overall average ROUGE1 scores and (2) average speed-up, i.e., the number of queries per
second for different summary lengths.
Results – Figure 3 shows ROUGE-1 scores of each approaches.
The number of queries per second for different summary lengths
were TextRank: 0.31, LexRank: 0.14, and Luhn: 11.29. We foundthat although the Luhn’s and TextRank algorithms had similar
ROUGE-1 scores, the number of queries per second or keywords
were generated was relatively higher in the case of Luhn’s algorithm.
The limitation of this study is that human intervention is often
required since summary evaluation is considered subjective.

6CONCLUSION

This paper presented a concept that can potentially improve thecognitive capability of a person reading a digital document. We
proposed an intelligent reading assistant system using a text summarization technique. In the future, we aim to improve our system
by incorporating a stronger text preprocessing pipeline to eliminate errors. We intend to explore several other summarization
techniques, including abstractive ones.

Text Summary Augmentation for Intelligent Reading AssistantREFERENCES[1] Mehdi Allahyari, Seyedamin Pouriyeh, Mehdi Assefi, Saeid Safaei, Elizabeth DTrippe, Juan B Gutierrez, and Krys Kochut. 2017. Text summarization techniques:a brief survey. arXiv preprint arXiv:1707.02268 (2017).
[2] Ralf Biedert, Georg Buscher, Sven Schwarz, Jörn Hees, and Andreas Dengel. 2010.
Text 2.0. In CHI’10 Extended Abstracts on Human Factors in Computing Systems.
4003–4008.
[3] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert:Pre-training of deep bidirectional transformers for language understanding. arXiv
preprint arXiv:1810.04805 (2018).
[4] Günes Erkan and Dragomir R Radev. 2004. Lexrank: Graph-based lexical centrality
as salience in text summarization. Journal of artificial intelligence research 22(2004), 457–479.
[5] Shoya Ishimaru, Ko Watanabe, Nicolas Großmann, Carina Heisel, Pascal Klein,
Yutaka Arakawa, Jochen Kuhn, and Andreas Dengel. 2018. Demonstration ofHyperMind Builder: Pervasive User Interface to Create Intelligent InteractiveDocuments.
[6] Soumy Jacob, Shoya Ishimaru, Syed Saqib Bukhari, and Andreas Dengel. 2018.
Gaze-based interest detection on newspaper articles. In Proceedings of the 7thWorkshop on Pervasive Eye Tracking and Mobile Eye-Based Interaction. 1–7.
[7] Chin-Yew Lin. 2004. Rouge: A package for automatic evaluation of summaries.
In Text summarization branches out. 74–81.
[8] Hui Lin and Vincent Ng. 2019. Abstractive summarization: A survey of the stateof the art. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33.

AHs ’21, February 22–24, 2021, Rovaniemi, Finland

9815–9822.
[9] Hans Peter Luhn. 1957. A statistical approach to mechanized encoding andsearching of literary information. IBM Journal of research and development 1, 4(1957), 309–317.
[10] Michael McCandless, Erik Hatcher, Otis Gospodnetić, and O Gospodnetić. 2010.
Lucene in action. Vol. 2. Manning Greenwich.
[11] Rada Mihalcea and Paul Tarau. 2004. Textrank: Bringing order into text. In Proceedings of the 2004 conference on empirical methods in natural language processing.
404–411.
[12] Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang,Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2019. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint
arXiv:1910.10683 (2019).
[13] Claudia Roda. 2011. Human attention and its implications for human–computerinteraction. 11–62.
[14] Thijs Scheepers. 2017. Improving the Compositionality of Word Embeddings.
Master’s thesis. Universiteit van Amsterdam, Science Park 904, Amsterdam,Netherlands.
[15] Kalpathy Subramanian. 2017. Product Promotion in an Era of Shrinking Attention
Span. International Journal of Engineering and Management Research Volume 7(03 2017), Pages 85 –91.
[16] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones,Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all
you need. In Advances in neural information processing systems. 5998–6008.