@article{see_get_2017,
  title =	 {Get {To} {The} {Point}: {Summarization} with {Pointer}-{Generator} {Networks}},
  shorttitle =	 {Get {To} {The} {Point}},
  note =	 "\url{http://arxiv.org/abs/1704.04368}",
  abstract =	 {Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text). However, these models have two shortcomings: they are liable to reproduce factual details inaccurately, and they tend to repeat themselves. In this work we propose a novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways. First, we use a hybrid pointer-generator network that can copy words from the source text via pointing, which aids accurate reproduction of information, while retaining the ability to produce novel words through the generator. Second, we use coverage to keep track of what has been summarized, which discourages repetition. We apply our model to the CNN / Daily Mail summarization task, outperforming the current abstractive state-of-the-art by at least 2 ROUGE points.},
  urldate =	 {2018-09-24},
  journal =	 {arXiv:1704.04368 [cs]},
  author =	 {See, Abigail and Liu, Peter J. and Manning, Christopher D.},
  month =	 apr,
  year =	 2017,
  note =	 "\url{http://arxiv.org/abs/1704.04368}",
  keywords =	 {Computer Science - Computation and Language},
  file =	 {arXiv\:1704.04368 PDF:/Users/waingram/Zotero/storage/C5H8DGS4/See et al. - 2017 - Get To The Point Summarization with Pointer-Gener.pdf:application/pdf;arXiv.org Snapshot:/Users/waingram/Zotero/storage/MAM2865V/1704.html:text/html}
}

@article{Harvey1990EstimationPF,
author = {Harvey, A. C. and Peters, S.},
title = {Estimation procedures for structural time series models},
journal = {Journal of Forecasting},

volume = {9},
number = {2},
pages = {89-108},
keywords = {Structural time series model, Forecasting, Kalman filter, Stochastic trend, Unobserved components model, EM algorithm},
doi = {10.1002/for.3980090203},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/for.3980090203},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/for.3980090203},
abstract = {Abstract A univariate structural time series model based on the traditional decomposition into trend, seasonal and irregular components is defined. A number of methods of computing maximum likelihood estimators are then considered. These include direct maximization of various time domain likelihood function. The asymptotic properties of the estimators are given and a comparison between the various methods in terms of computational efficiency and accuracy is made. The methods are then extended to models with explanatory variables.},
year = {1990}
}


@article{Additive,
 ISSN = {01621459},
 URL = {http://www.jstor.org/stable/2289439},
 abstract = {Generalized additive models have the form η( x) = α + ∑ fj(xj), where η might be the regression function in a multiple regression or the logistic transformation of the posterior probability $\Pr(y = 1 \mid \mathbf x)$ in a logistic regression. In fact, these models generalize the whole family of generalized linear models η( x) = β' x, where η( x) = g(μ( x)) is some transformation of the regression function. We use the local scoring algorithm to estimate the functions fj(xj) nonparametrically, using a scatterplot smoother as a building block. We demonstrate the models in two different analyses: a nonparametric analysis of covariance and a logistic regression. The procedure can be used as a diagnostic tool for identifying parametric transformations of the covariates in a standard linear analysis. A variety of inferential tools have been developed to aid the analyst in assessing the relevance and significance of the estimated functions: these include confidence curves, degrees of freedom estimates, and approximate hypothesis tests. The local scoring algorithm is analogous to the iterative reweighted least squares algorithm for solving likelihood and nonlinear regression equations. At each iteration, an adjusted dependent variable is formed and an additive regression model is fit using the backfitting algorithm. The backfitting algorithm cycles through the variables and estimates each coordinated function by smoothing the partial residuals.},
 author = {Trevor Hastie and Robert Tibshirani},
 journal = {Journal of the American Statistical Association},
 number = {398},
 pages = {371--386},
 publisher = {[American Statistical Association, Taylor & Francis, Ltd.]},
 title = {Generalized Additive Models: Some Applications},
 volume = {82},
 year = {1987}
}



@inproceedings{hingu_automatic_2015,
  title =	 {Automatic text summarization of {Wikipedia} articles},
  doi =		 {10.1109/ICCICT.2015.7045732},
  abstract =	 {The main objective of a text summarization system is to identify the most important information from the given text and present it to the end users. In this paper, Wikipedia articles are given as input to system and extractive text summarization is presented by identifying text features and scoring the sentences accordingly. The text is first pre-processed to tokenize the sentences and perform stemming operations. We then score the sentences using the different text features. Two novel approaches implemented are using the citations present in the text and identifying synonyms. These features along with the traditional methods are used to score the sentences. The scores are used to classify the sentence to be in the summary text or not with the help of a neural network. The user can provide what percentage of the original text should be in the summary. It is found that scoring the sentences based on citations gives the best results.},
  booktitle =	 {2015 {International} {Conference} on {Communication}, {Information} {Computing} {Technology} ({ICCICT})},
  author =	 {Hingu, D. and Shah, D. and Udmale, S. S.},
  month =	 jan,
  year =	 2015,
  keywords =	 {Internet, Feature extraction, neural nets, Neural networks, text analysis, automatic text summarization, Computers, Electronic publishing, Encyclopedias, Frequency, Natural Language, neural network, Python, sentence classification, sentence scoring, sentence tokenization, stemming operations, text feature identification, text preprocessing, Text summarization, Web sites, Wikipedia articles},
  pages =	 {1--4},
  file =	 {IEEE Xplore Abstract Record:/Users/waingram/Zotero/storage/8SX3TBHK/7045732.html:text/html;IEEE Xplore Full Text PDF:/Users/waingram/Zotero/storage/P993PZ9Q/Hingu et al. - 2015 - Automatic text summarization of Wikipedia articles.pdf:application/pdf}
}

@article{khatri_abstractive_2018,
  title =	 {Abstractive and {Extractive} {Text} {Summarization} using {Document} {Context} {Vector} and {Recurrent} {Neural} {Networks}},
  note =	 "\url{http://arxiv.org/abs/1807.08000}",
  abstract =	 {Sequence to sequence (Seq2Seq) learning has recently been used for abstractive and extractive summarization. In current study, Seq2Seq models have been used for eBay product description summarization. We propose a novel Document-Context based Seq2Seq models using RNNs for abstractive and extractive summarizations. Intuitively, this is similar to humans reading the title, abstract or any other contextual information before reading the document. This gives humans a high-level idea of what the document is about. We use this idea and propose that Seq2Seq models should be started with contextual information at the first time-step of the input to obtain better summaries. In this manner, the output summaries are more document centric, than being generic, overcoming one of the major hurdles of using generative models. We generate document-context from user-behavior and seller provided information. We train and evaluate our models on human-extracted-golden-summaries. The document-contextual Seq2Seq models outperform standard Seq2Seq models. Moreover, generating human extracted summaries is prohibitively expensive to scale, we therefore propose a semi-supervised technique for extracting approximate summaries and using it for training Seq2Seq models at scale. Semi-supervised models are evaluated against human extracted summaries and are found to be of similar efficacy. We provide side by side comparison for abstractive and extractive summarizers (contextual and non-contextual) on same evaluation dataset. Overall, we provide methodologies to use and evaluate the proposed techniques for large document summarization. Furthermore, we found these techniques to be highly effective, which is not the case with existing techniques.},
  urldate =	 {2018-09-24},
  journal =	 {arXiv:1807.08000 [cs]},
  author =	 {Khatri, Chandra and Singh, Gyanit and Parikh, Nish},
  month =	 jul,
  year =	 2018,
  note =	 {arXiv: 1807.08000},
  keywords =	 {Computer Science - Computation and Language},
  file =	 {arXiv\:1807.08000 PDF:/Users/waingram/Zotero/storage/MDUBWHAM/Khatri et al. - 2018 - Abstractive and Extractive Text Summarization usin.pdf:application/pdf;arXiv.org Snapshot:/Users/waingram/Zotero/storage/YFC9TI8Y/1807.html:text/html}
}


@article{10.2307/24993660,
 ISSN = {00368733, 19467087},
 URL = {http://www.jstor.org/stable/24993660},
 author = {Brewster Kahle},
 journal = {Scientific American},
 number = {3},
 pages = {82--83},
 publisher = {Scientific American, a division of Nature America, Inc.},
 title = {PRESERVING THE INTERNET},
 volume = {276},
 year = {1997}
}



@InProceedings{10.1007/978-3-642-33290-6_14,
author="SalahEldeen, Hany M.
and Nelson, Michael L.",
editor="Zaphiris, Panayiotis
and Buchanan, George
and Rasmussen, Edie
and Loizides, Fernando",
title="Losing My Revolution: How Many Resources Shared on Social Media Have Been Lost?",
booktitle="Theory and Practice of Digital Libraries",
year="2012",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="125--137",
abstract="Social media content has grown exponentially in the recent years and the role of social media has evolved from just narrating life events to actually shaping them. In this paper we explore how many resources shared in social media are still available on the live web or in public web archives. By analyzing six different event-centric datasets of resources shared in social media in the period from June 2009 to March 2012, we found about 11{\%} lost and 20{\%} archived after just a year and an average of 27{\%} lost and 41{\%} archived after two and a half years. Furthermore, we found a nearly linear relationship between time of sharing of the resource and the percentage lost, with a slightly less linear relationship between time of sharing and archiving coverage of the resource. From this model we conclude that after the first year of publishing, nearly 11{\%} of shared resources will be lost and after that we will continue to lose 0.02{\%} per day.",
isbn="978-3-642-33290-6"
}



@INPROCEEDINGS{6970226,
author={T. {Agata} and Y. {Miyata} and E. {Ishita} and A. {Ikeuchi} and S. {Ueda}},
booktitle={IEEE/ACM Joint Conference on Digital Libraries},
title={Life span of web pages: A survey of 10 million pages collected in 2001},
year={2014},
volume={},
number={},
pages={463-464},
keywords={Internet;meta data;Web sites;Web page life span;survival survey;Japanese;NTCIR-3;metadata;Internet archive wayback machine;memento;Web pages;Internet;Educational institutions;Libraries;Uniform resource locators;Estimation;Standards;Web Page Life Span;Web Archiving;Digital Preservation;Internet Archive},
doi={10.1109/JCDL.2014.6970226},
ISSN={},
month={Sep.},}

@inproceedings{radinsky_predicting_2013,
	address = {New York, NY, USA},
	series = {{WSDM} '13},
	title = {Predicting {Content} {Change} on the {Web}},
	isbn = {978-1-4503-1869-3},
	url = {http://doi.acm.org/10.1145/2433396.2433448},
	doi = {10.1145/2433396.2433448},
	abstract = {Accurate prediction of changing web page content improves a variety of retrieval and web related components. For example, given such a prediction algorithm one can both design a better crawling strategy that only recrawls pages when necessary as well as a proactive mechanism for personalization that pushes content associated with user revisitation directly to the user. While many techniques for modeling change have focused simply on past change frequency, our work goes beyond that by additionally studying the usefulness in page change prediction of: the page's content; the degree and relationship among the prediction page's observed changes; the relatedness to other pages and the similarity in the types of changes they undergo. We present an expert prediction framework that incorporates the information from these other signals more effectively than standard ensemble or basic relational learning techniques. In an empirical analysis, we find that using page content as well as related pages significantly improves prediction accuracy and compare it to common approaches. We present numerous similarity metrics to identify related pages and focus specifically on measures of temporal content similarity. We observe that the different metrics yield related pages that are qualitatively different in nature and have different effects on the prediction performance.},
	urldate = {2019-09-22},
	booktitle = {Proceedings of the {Sixth} {ACM} {International} {Conference} on {Web} {Search} and {Data} {Mining}},
	publisher = {ACM},
	author = {Radinsky, Kira and Bennett, Paul N.},
	year = {2013},
	keywords = {web change prediction, web crawling, web dynamics},
	pages = {415--424}
}

@inproceedings{Saad:2010:UVP:1754239.1754287,
 author = {Saad, Myriam Ben and Gan\c{c}arski, St{\'e}phane},
 title = {Using Visual Pages Analysis for Optimizing Web Archiving},
 booktitle = {Proceedings of the 2010 EDBT/ICDT Workshops},
 series = {EDBT '10},
 year = {2010},
 isbn = {978-1-60558-990-9},
 location = {Lausanne, Switzerland},
 pages = {43:1--43:7},
 articleno = {43},
 numpages = {7},
 url = {http://doi.acm.org/10.1145/1754239.1754287},
 doi = {10.1145/1754239.1754287},
 acmid = {1754287},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {change detection, visual page analysis, web archiving, web crawling},
} 


@INPROCEEDINGS{styleTreeSim,
author={T. {Gowda} and C. A. {Mattmann}},
booktitle={2016 IEEE 17th International Conference on Information Reuse and Integration (IRI)},
title={Clustering Web Pages Based on Structure and Style Similarity (Application Paper)},
year={2016},
volume={},
number={},
pages={175-180},
keywords={hypermedia markup languages;Internet;pattern clustering;trees (mathematics);cluster analysis task;semantic meaning;Web page structure;Web page style;categorization;cleaning;schema detection;automatic extractions;structural similarity;HTML pages;tree edit distance measure;DOM trees;stylistic similarity;hyper text markup language;Web pages;Vegetation;HTML;Cascading style sheets;World Wide Web;Data models;Videos;jaccard;similarity;tika;metadata;clustering;documents},
doi={10.1109/IRI.2016.30},
ISSN={null},
month={July},}

@inproceedings{law2012structural,
  title={Structural and visual similarity learning for web page archiving},
  author={Law, Marc Teva and Gutierrez, Carlos Sureda and Thome, Nicolas and Gan{\c{c}}arski, St{\'e}phane and Cord, Matthieu},
  booktitle={2012 10th International Workshop on Content-Based Multimedia Indexing (CBMI)},
  pages={1--6},
  year={2012},
  organization={IEEE}
}

@INPROCEEDINGS{serverChange,
author={L. {Meegahapola} and R. {Alwis} and E. {Nimalarathna} and V. {Mallawaarachchi} and D. {Meedeniya} and S. {Jayarathna}},
booktitle={2017 Seventeenth International Conference on Advances in ICT for Emerging Regions (ICTer)},
title={Detection of change frequency in web pages to optimize server-based scheduling},
year={2017},
volume={},
number={},
pages={1-7},
keywords={Internet;scheduling;web pages;notification systems;change frequency detection;server-based scheduling;Internet;static time intervals;Web pages;Crawlers;Servers;Monitoring;Schedules;Algorithm design and analysis;Internet;web page;change detection and notification systems;crawling;change frequency;server-side scheduling},
doi={10.1109/ICTER.2017.8257791},
ISSN={2472-7598},
month={Sep.},}

@inproceedings{pehlivan2010vi,
  title={Vi-DIFF: Understanding web pages changes},
  author={Pehlivan, Zeynep and Ben-Saad, Myriam and Gan{\c{c}}arski, St{\'e}phane},
  booktitle={International Conference on Database and Expert Systems Applications},
  pages={1--15},
  year={2010},
  organization={Springer}
}

@article{EditTreeDist,
author = {Zhang, Kaizhong and Shasha, Dennis},
year = {1989},
month = {12},
pages = {1245-1262},
title = {Simple Fast Algorithms for the Editing Distance Between Trees and Related Problems},
volume = {18},
journal = {SIAM J. Comput.},
doi = {10.1137/0218082}
}


@article{prophet,
author = {Taylor, Sean and Letham, Benjamin},
year = {2017},
month = {09},
pages = {},
title = {Forecasting at Scale},
volume = {72},
journal = {The American Statistician},
doi = {10.1080/00031305.2017.1380080}
}

@book{cristianini_shawe-taylor_2000, place={Cambridge}, title={An Introduction to Support Vector Machines and Other Kernel-based Learning Methods}, DOI={10.1017/CBO9780511801389}, publisher={Cambridge University Press}, author={Cristianini, Nello and Shawe-Taylor, John}, year={2000}}

@INPROCEEDINGS{5662320,
author={D. {Yang} and J. {Song}},
booktitle={2010 International Conference on Web Information Systems and Mining},
title={Web Content Information Extraction Approach Based on Removing Noise and Content-Features},
year={2010},
volume={1},
number={},
pages={246-249},
abstract={This paper presents an improved approach to extract the main content from web pages. There are a good many financial news pages which have so many links that the algorithms mainly based on link density have poor performance in extracting main content. To solve this problem, we put forward an extracting main content method which firstly removes the usual noise and the candidate nodes without any main content information from web pages, and makes use of the relation of content text length, the length of anchor text and the number of punctuation marks to extract the main content. In this paper, we focus on removing noise and utilization of all kinds of content-characteristics, experiments show that this approach can enhance the universality and accuracy in extracting the body text of web pages.},
keywords={information retrieval;Internet;Web content information extraction;Web pages;financial news pages;link density;extracting main content method;content text length;anchor text length;punctuation mark;noise content removal;Web pages;Noise;Data mining;Feature extraction;Accuracy;Navigation;Information processing;information extraction;web page content extraction;removing noise content},
doi={10.1109/WISM.2010.82},
ISSN={},
month={Oct},}

@article{Schulman2017ProximalPO,
  title={Proximal Policy Optimization Algorithms},
  author={John Schulman and Filip Wolski and Prafulla Dhariwal and Alec Radford and Oleg Klimov},
  journal={ArXiv},
  year={2017},
  volume={abs/1707.06347}
}

@article{Mnih2013PlayingAW,
  title={Playing Atari with Deep Reinforcement Learning},
  author={Volodymyr Mnih and Koray Kavukcuoglu and David Silver and Alex Graves and Ioannis Antonoglou and Daan Wierstra and Martin A. Riedmiller},
  journal={ArXiv},
  year={2013},
  volume={abs/1312.5602}
}

@article{1606.01540,
  author    = {Greg Brockman and
               Vicki Cheung and
               Ludwig Pettersson and
               Jonas Schneider and
               John Schulman and
               Jie Tang and
               Wojciech Zaremba},
  title     = {OpenAI Gym},
  journal   = {CoRR},
  volume    = {abs/1606.01540},
  year      = {2016},
  url       = {http://arxiv.org/abs/1606.01540},
  archivePrefix = {arXiv},
  eprint    = {1606.01540},
  timestamp = {Fri, 08 Nov 2019 12:51:06 +0100},
  biburl    = {https://dblp.org/rec/bib/journals/corr/BrockmanCPSSTZ16},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@misc{stable-baselines,
  author = {Hill, Ashley and Raffin, Antonin and Ernestus, Maximilian and Gleave, Adam and Kanervisto, Anssi and Traore, Rene and Dhariwal, Prafulla and Hesse, Christopher and Klimov, Oleg and Nichol, Alex and Plappert, Matthias and Radford, Alec and Schulman, John and Sidor, Szymon and Wu, Yuhuai},
  title = {Stable Baselines},
  year = {2018},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/hill-a/stable-baselines}},
}