News articles template summarization and categor
Contents
1. 10 https www python org doc essays comparisons Resources used http home adelphi edu siegfried cs480 ReqsDoc pdf http wwwis win tue nl 2R690 projects spingrid srd pdf Figures http image slidesharecdn com meetsolrforthefirsttimeagain 141013005741 conversion gateQ2 95 meet solr for the tirst again 18 638 jpg cb 1413179929 http heliosearch org wp content uploads 2012 08 solr_admin png http www 1bm com developerworks library os weka2 weka dataS jpg https users soe ucsc edu kunqian logos csharp png https 1h4 googleusercontent com OfbEaUyOzgs Uycg_aNlvH AAAAAAAAG5s vvq4LmANws0 s0 java logo png http www blancocuaresma com s static mages python logo png http www valiantsolutions com images infosec jpg 8 202. To do this efficiently we will need to understand the underlying architecture driving this tool March requires us to classify news articles so we can determine which algorithm will be most accurate 18 to minimize any categorization issues We will be displaying a summary along with the classification results which will require more Solr and Fusion manipulation Result validation will take a large amount of time since we currently do not have any computer related automation This means developers will manually sift through a sample of the data It will be immediately apparent 1f the summarization and categorization categories display properly so there will be no need for further testing 8 Lessons Learned So far we have been able to keep to the timeline All work that our contract stated which had to be finished by May is complete We encountered various problems developing this system A student who was supposed to help develop tags was unable to aid us Team members also became sick which meant that some of the team meetings had to be held online To keep to the timeline the team worked extra to cover any deficits in other people s work Even though a student was unable to help us with some work we still kept to the schedule The timeline states the work we have left integrate Fusion summarizations and debug the project 9 Conclusion We have gotten everything together and working smoothly as per requested from the client The result ar
3. in the technical community 4 Design We are using Lucidworks Fusion for this program It has a lot of capabilities that we are using mainly for indexing Lucidworks Fusion AS Lucidworks Intelligent Search Services API Bscommeandaton Modulo i 5 Analytics 5a ros Enrichment Apache WZ ai oe a Services gt gt Solr od a all i nalytics Store Connector Framework Figure 3 Lucidworks Fusion capabilities and relations Fusion is built off of the Solr Apache system We use Solr for querying after we have indexed the news items E GB olr System 9 Logging Start 5 minutes ago Physical Memory 73 0 IB Core Admin Host 192 168 1 102 E Java Properties y cwo opt code lusolr solr example Thread Dump lab Instance opt code lusolr solr example solr collection1 5 84 GB la Data opt code lusolr solr example solr collection1 data daia 306 PPP collection1 ld Index opt code lusolr solr example solr collection1 data index Versions EHE Z solr spec 5 0 0 2012 08 15 13 17 06 77 E solr impl 5 0 SNAPSHOT 1373442M yonik 2012 08 15 13 17 06 incluir t Macs _ lucene spec 5 0 SNAPSHOT lucene impl 5 0 SNAPSHOT 1373442 yonik 2012 08 15 13 15 15 153 E JVM ma JVM Memory 147 3 Runtime Java HotSpot TM 64 Bit Server VM 20 8 b03 424 W Processors 4 18 24 MB 81 06 MB Documentation AR Issue Tracker 2 IRC Channel Community forum lo Solr Query Syntax Figure 4 Solr interface used for quer
4. is approachable 7 Timeline The team plans to work on the project consistently until the end of the semester in May 2015 We will meet every Wednesday afternoon for two hours in Torgeson to discussed reviewed work and to continue developing the project We are using an Agile program development style where we continually change and update the project to fit any problems design needs or deadlines This is necessary with our continual feedback from the professor and client If we were to use a Waterfall style method we might not be able to use our client s and professor s feedback This style is very sequential and once we finish a portion of the project we cannot make any changes later on We have already outlined our proposed timeline on how we plan to complete this project in time Learn Solr and Fusion Implement and modify fusion schemas to include extra fields eClassify news articles Mare h Connect summarization and classfication results with Fusion i Validate results Test Prepare final report Finaltouchups Figure 11 Timeline of the implementation of the project The developers will learn Solr and Fusion by reading the companies websites Afterwards we will then try to make small programs using what we learned At a point where we are comfortable using this technology we will implement these tools in our project Fusion has a predefined template which we will need to modify to allow us to include extra fields
5. the data to make sure that it works for the entirety of the data set 3 5 Sketch of Application Process CSV Feature CSV on Raw Text Classification Collection set Figure 1 Processing of the PDF news article through the application Information Extraction y Build Corpus y Generate Summaries y Index Collection with Fusion Fusion Search Results Figure 2 Developer s Data Flow 3 6 Possibilities for Future Program Use We are only planning to implement this program for the Arabic language We hope that the work can be extended for use with more languages in the future We are working with Arabic which means the code we write has to be language independent because some of the programmers do not speak or read Arabic Optimistically countries like France or Australia that are trying to analyze news related issues could sort through news articles under a certain category and then use the information as meta data It also helps that this is a scholastically created project so there are no monetary sponsors that could influence the creation of the project News in America 1s notoriously bi partisan and hopefully this will be a way to view news trends without trying to sway the end user to a particular viewpoint more specifically the viewpoint of the sponsor It is also beneficial that we are using Java as a language to create the program because it 1s one of the most widely used programming languages
6. the software and modification needed to be made there are some prior knowledge that is required in order to understand the scope of the application Developer should be well versed in a programming language preferably Java and Python and have at least a basic understanding of natural language processing and machine learning in order to understand the underlying concept used by the tools to help us achieve a solution The following tools we have used Java 1 8 or greater SDK from Oracle Python 3 x or greater SDK from Python Weka 3 6 12 or greater from University of Waikato RenA Arabic NER provided from Souleiman Ayoub and Tarek Kanan ALDA Arabic Latent Dirichlet Allocation provided from Souleiman Ayoub and Tarek Kanan e Fusion from LucidWorks 3 2 Collection We will also be providing the collection of roughly 120 000 articles which will can be used to alter modify or append to if necessary depending on end goal These articles are encoded in UTF 8 and should be processed using UTF 8 Encoding Decoding most languages such as Java and Python provides support BufferedReader Java API and codecs Python API for more info The use of the following tools NER LDA and classification will be used to help us generate a summary to provided in the fusion schema along with the article Each of these articles will be classified using Weka more explanation will be provided below 3 3 NER There are two ways to use the NER we
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8. News Summarization Building a Fusion a Solr based system special collection News articles template summarization and categorization Souleiman Ayoub Julia Freeman Tarek Kanan Edward Fox Computer Science 4624 Spring 2015 Virginia Tech Blacksburg VA 24061 Email siayoub juliaf tarekk fox O vt edu March 15 2015 Table of Contents COVE Palit 1 Table or COMEN S isis 2 Ele AAA e E EE nm en Marre Seiten mene re enter eer meee heen ene enn eer re eee 3 Tal Ol IQUGCS act a batas 3 Te Requirements tii a dile 4 dlls POS ACU shearers dai ld 4 2 ODECE aaa toos 4 eS USER ROGS scapes seats asc yrs idee eae eee tea ota ata een cere en aces 4 A A O E E 4 Bis IAPPROACA cera dai EE raid 5 BOs Miltone Sci iii 5 2 USEMI Ienn A r teendaamserncecieds 5 Si Developers Manual yesss iaa 5 lis RSE QquiSile NOW Cr dd eae 5 S2 A E a 6 So NE 6 34 CUNAS e bs Es ee 6 3 05 Sket hor Applicaton ProOCES Sisi etc 7 3 6 Possibilities tor Future Program US Build 8 A SSVI apes acs ahd sare tke eh cll hapa hel a ale dad Mira iil asda c cle haute De haute lelahona cided Mice lded itd 8 A Ns A menatcnds edsende DEE DEE OE E 12 Asta Programming Languages tcs 12 Ane OOS ana LIDFAnleS EMDIOY CG casar ibc s 13 Dalles GOO IACDOSILOLY PIAS sra iiela 13 ie e T O 14 4 1 Text and Attribute Extraction ccconcconcocnconncocnoonnonnnonnnnononononononononanonononanenaninnnnns 14 qe SUMMA ZATION saienisi i a a a 14 Aud MOCKING DOCU
9. achine learning Solr retrieval system Fusion LDA s LU NER s 21 and Java to create extract and generate the final summaries and to provide the user the ability to see the the articles and the summaries all in one place 5 5 Approach 5 6 Milestones e By February we will work on extracting the articles main attributes like categories Named Entities and Topics using machine learning tools NERs etc 9 and Fusion and implement and modify Fusion schemas e By March we will learn Solr to include extra fields e By April we will connect summarization results with Fusion to enable automation Then we will validate the results of the programs and prepare a final report of what we did what we were successful with and what we might not be able to complete eo We reserve the entirety of May for final touchups debugging and user testing 2 User Manual There is currently no existing system for what we are attempting to do We are piecing together a few existing algorithms and methods for topic generating like LDA Latent Dirichlet Allocation and for named entity extraction like NER Named Entity Recognizer but we have to alter them to fit our project needs like handling the Arabic language which can be very challenging Hopefully in the future users will be able to see trends in news data which can help with security or data mining 3 Developer s Manual 3 1 Prerequisite Knowledge In order to use
10. ar on the same page as the article title and category 4 3 Indexing Documents We will create a way of sorting and identifying the documents so that we can access them in a manner of our choosing 4 4 Testing This phase will require many hours of manual testing to ensure that the algorithms work correctly We will also step through the program to ensure that it is reacting correctly and meets all of our project specifications 14 5 Prototyping 5 1 Classification with Weka In order to fully utilize implementation of fusion we need to begin by classifying the news articles for the purposes of generating summarization for each article based on its categories Each file must consists on of the following category as show in the table below Table 1 Categories Social Society In order to classify the collection of articles we need to choose a random sample to build our feature set We are given a random sample Thanks to Tarek of 2 000 articles where each categories consists of 400 articles In order to build our feature set we will first need to collect featured words or bag of words from all of the articles unique words and elimination of stopwords An example can be seen in the table below Table 2 Sample Header of Feature Set Suppose our training set consists of articles regarding to technology we know that each article has a specific label such that for each words in the article if it triggers a word that
11. e as expected The interface runs seamlessly and shows the results based on the search criteria The application is up an running on the clients machine and has been tested The documents that were parsed have been imported into Fusion and indexed along with the modified schema file which should now show the results of the extra fields 10 Acknowledgments We would like to acknowledge Dr Edward Fox the class professor for his guidance throughout the class We also would like to thank our client and mentor Tarek Kanan for all his help the creation of this project He can be reached at tarekk vt edu A special thanks goes to Lucidworks the Fusion creator company for answering some of our questions and for guiding us through the Fusion part of this work This work was made possible by NPRP grant 4 029 1 007 from the Qatar National Research Fund a member of Qatar Foundation 19 11 References 1 LDA http en wikipedia org wiki Latent_Dirichlet_allocation 2 NER http en wikipedia org wiki Named entity_recognition 3 Weka http www cs waikato ac nz ml weka 4 LucidWorks Fusion http lucidworks com product fusion 5 Solr https wiki apache org solt 6 PDF Parsing http www pdtfparser org 7 Text Summarization http en wikipedia org wiki Automatic_summarization 8 Java BufferedReader http docs oracle com javase 8 docs api java io BufferedReader html 9 Python Codecs https docs python org 3 library codecs html
12. eting these criteria itech dt as headed iat sitll 20 duen dd tri rin ir este rd Lab Figure 9 Sample result of Summarized Article 16 5 3 Fusion Once we have collected our summaries for our articles We can beging importing them to Fusion Fusion has a very simple UI that allows us to import a persistence and it will automatically index the article on it s own After importing our local persistence to fusion we can begin searching below we can see some sample result Owner Parsing Lw data source collection Group Lw batch Content Character set Parent Lw data source pipeline Lw data source type okami ok antcle_summary_document okami 2b210cf7ed4 decaela249ea1e37327 Article 4 oyal lolas aged g pyu gills Aala pli pahal ATY a ghal palaa sae T 4 gill daly lay a calal de cy ame a pathy othe li les ll hae ules mama ad oy gal ll aia gal pce ll ab lio Fi AA APA EF a y Aaga h Al glaa g jo TV ND jo aliiu a Y a golly ll Oy oda ag clo de eN a pa LA al ply Da gi y e A all y lali dy all cue Yh al ad Al las a y AE pia a gee 435 1234 JA ye lg aig paisal y lll Aile je par pill gal y al TY e a tone adlan K Gag oy al flac ig pa AN 3 del als OY del oda il a jala aa al Sa al Sil jl aa aia cfg a il oY gla a aso DA gya 9 989 a AS Gl A SI grill oji aana de pla pan hy ala al chjai Ls all ai pei Y a logo sana a Ny lr elo y ela pull ad E pio fly jae all dul ys qual dole aid pine a yiel
13. exists in the feature said a boolean will be placed in the cell in respect to the word For example in the table below continueing from table 2 suppose we have an article about an Apple product review It is expected that Apple Phone and perhaps computer will have a boolean flag Suppose we have a technology category Table 3 Sample overview of features for Apple Review apple_review technology 1 0 1 1 0 0 As we continue to do this for the 2 000 articles with it s appropriate label we can begin to train using various classification models including SMO SVM NaiveBayes and Random Forest each using 10 fold cross validation Table 4 Average F1 Measure per Model SMO 84 38 19 31 11 179 15 We have opted to use SMO as it provided higher results for classifying labels correctly After confirming the selection of our model We can now begin to classify our dataset of 120 000 articles using SMO However as previously stated we need to extract out bag of words from each articles and flag the booleans to begin classification Once all the articles have been labeled we ll begin to put together the results to form a summary of the article 5 2 Bringing it together For each article we are given a CSV file that contains the category as well as other information that has been extracted using NER to collect entities LDA to collect articles topics titles and author Below is a screenshot of a sample file me
14. have provided a python script that will quickly generate named entity extraction if needed for testing purpose the python script is called ner py and can be used as follows gt ner py F lt text_file gt t lt PERS LOC ORG gt gt lt output gt The command above will generate a text file which consists of named entities based on the given file t are the named entity given to extract However for a more advanced extraction such as n gram solution and advanced structures Please refer to the class arabic ner RenA which consists of the option to request more features 3 4 Current Progress We are currently trying to perfect a way to parse text documents into ARFFs Attribute Relation Files that will be used as input to the machine learning program This type of document ARFF is ideal for the project because we can more easily scan the document categorize and summarize it as opposed to creating a whole other program to parse text documents The conversion is not perfected yet because some articles might only contain pictures which are of no use to the program We also remove any stop words which are words which are placeholders like the or a but in Arabic This means we will have to go through and remove any empty files after they have been converted To ensure that we are creating ARFFs properly and they are categorized properly ex a soccer article is not put into the Art category we will have to manually test a sample of
15. igure 5 Weka interface used for data MINING cocccoccnccccccccncconconcnnnononncnnnnnonnnncnncnnnrnnnannnnnnos 10 Figure 6 Article view with invisible backend tags cccoccccocncoccccococonnccncncnnnonnnncnncnoncnnnanonnnnos 11 Figure 7 From left to right this is the typical best run time speed of CF Java and Python 12 Figure 8 Security is a major issue for any project ooncccconncconcnconcnconcnnoncnnnoncncnncnnnnnrnnonnnonarinnns 14 Figure 9 Sample result of Summarized Article cooocccconcncoccnoccnncononccnnncnonnnnnnnonannnnnacnnnns 16 Figure 10 Sample result of result in FUSION cccocccccccnccccnnononcconnnonnnnncnnnnnnnnncnnnnonnnnonnnnnnnncnnnos 17 Figure 11 Timeline of the implementation of the project ocoooccoccccoccconnoconocnnncnnncnnncnnnnononos 18 1 Requirements 5 1 Abstract This project will attempt to take Arabic PDF news articles and end with results from our new program that index categorize and summarize them We will fill out a template to summarize news articles with predetermined attributes These values will be extracted using named entities recognizer NER which will recognize organizations and people topic generation using an LDA lI algorithm and direct information extraction from news articles authors and dates We will use Fusion LucidWorks a Solr based system to help with the indexing of our data set and provide an interface for the use
16. l s Summary a AMan TY il ger aro pue Leland gal y ys all Ala oll io dal 74 gill a ant UPA iail JE CH 1an aall y Lidl pala y aiall E Cy tame ASII Jyala A aluco y jala ja glie sana Eal eE auc al cl all ec Yh po ll as shah pole gall all all e patil al As gall pi dass il es a e e oa gall la colas doll sales Aja Read less UTF 8 home okaml Tusion persistence conn_solr lucid anda Tile LE Laa e dilo y pla g ld home okamil Tusion persistence 135f6849 35a 4319 9090 ebeS9Ucbed Read more X parsed by org apache tka parsertxt TXT Parser Fetched date 2015 03 2 122 34 023Z Last modified 2015 03 2 122 29582 Raw content FU OXJOaWNsZTok2KfZ2hNiv2YfrrdmHLSAgxXSAgOlAg2Ln4gtiviCDZh dis2Y TY Read more Figure 10 Sample result of result in Fusion We have modified the schema to allow adding and removing some fields by adding an extra field of summary and removing unnecessary field such as source link We have also helped test the new interface for Fusion 17 6 Testing We have done various form of testing to help ensure stability of our application For functional testing we have tested the schema file modification and extracting the text files to XML file we also did a functional and unit testing for indexing to make sure that everything searches properly We have done majority of our integration and usability testing on our interface to make sure that everything integrates well and
17. ler projects because there is less of a time difference but if someone chooses to expand upon this project in the future we would like to enable them to make significant changes Visualc Java python Figure 7 From left to right this is the typical best run time speed of C Java and Python Java is platform independent which can be useful for others using or programming this project Unfortunately compared to other languages programmers are encouraged to use Object Oriented Programming when writing in Java which can take more time to write However it will be much easier for future developers to understand what is going on in the code and to work on it immediately 12 4 2 Tools and Libraries Employed We have already introduced the tools we will be using Weka Solr and Fusion They are all Java based which is complementary to us programming in Java Tools that are Java based are were written and created using Java Since we are using Java we will limit ourselves to using the built in libraries Java provides 5 1 Code Repository Plans We will not be using a program to manage commits There are a limited number of personnel working on this project so there 1s little likelihood that multiple people will attempt to write code at the same time Every person is working on different parts of the project so even 1f people are working on the project at the same time there is no chance that someone will overwrite another member s work
18. or that their code will be impacted by code updates Since this 1s not a massive project in regards to the number of people working on it we will only host on local machines and every individual will have their own local copy of code The final results of the program will be stored on a separate server We do not need to worry about many security issues for the project The largest problem we could encounter 1s a user accidentally or intentionally interacting with the source code for how the program works Since the project will be stored locally the user who changed the code will not impact any other users eliminating the need for login credentials The server should handle any unauthorized accesses or changes eliminating the responsibility of security for our program Security is normally a major issue but this program will not contain any sensitive data nor will it register users who use 1t so there is no need to worry about security 13 Figure 8 Security is a major issue for any project 5 1 Phases 4 1 Text and Attribute Extraction We need to write algorithms to extract the text and any relevant attributes to be able to categorize articles All other summarization goals are dependent on finishing this phase 4 2 Summarization We will provide a brief summary for each article so that if the title is not adequate for a user they will be able to read the summary as well We will alter the Fusion template to allow the summary to appe
19. r to search and browse the articles with their summaries Solr will be used for information retrieval We hope to end with a program that enables end users to sift through news articles quickly 5 2 Objective The summarized articles need to be archived in such a way that it can be retrieved to allow us and possibly future users to use With the use of Fusion we can archive these information to allow us to search and view the summarized articles However in order to achieve this we ll need to collect the information that exists in the article via tools such as an NER LDA and a form of classification to determine subject 1 e sport politics etc With these information we can use a template to help us summarize each articles 5 3 User Roles Each individual has a different role on the team The two students currently taking the Hypertext and Multimedia Capstone are Julia Freeman and Souleiman Ayoub Julia Freeman will be a developer as well as a peer evaluator Souleiman Ayoub will also be a developer Tarek Kan an will be a mentor and team leader 5 4 Intent By May 8 2015 we hope to have an application that can 1 Parse Arabic PDF news sources and extract articles 2 Obtain useful information from the parsed articles 3 Use the extracted information to fill in empty templates generating Arabic news article summaries 4 Enable the user to browse articles along with their summaries We will be using Weka m
20. ying We are also use Weka for data classification It was developed by the university of Waikato 1 A J m e me lt A a a gt SPA The user will have an article view and their will exist tags for every article that the user will not be able to see but will allow the article to be categorized 10 11 4 1 Implementation 4 1 Programming Languages Java 1s the only programming language used in this project We chose this language over other prevalent languages like C or Python for a couple of reasons C takes more time to type because the programmer must directly allocate any memory used for the project but this means that it will hopefully be faster and more efficient because the user manages all the memory A large potential problem using C is memory leaks if the developer did not program the application correctly This means that the application will not reuse allocated memory and can eventuall y run out of usable memory Python is typically easier to write than Java but this tradeoff means that will it will most likely run slower than its Java counterpart 1 Java seemed to be a good middle ground for ease of writing the code and the speed which it will run It also helped that the developers have many years of experience writing in Java compared to any other language The running speed of a program might not be an issue for smal
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