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ETD Chapter Summarization

Abstract

Team 16 in the fall 2018 course "CS 4984/5984 Big Data Text Summarization," in partnership with the University Libraries and the Digital Library Research Laboratory, prepared a corpus of electronic theses and dissertations (ETDs) for students to study natural language processing with the power of state-of-the-art deep learning technology. The ETD corpus is made up of 13,071 doctoral dissertations and 17,890 master theses downloaded from the University Libraries’ VTechWorks system. This particular study is designed to explore big data summarization for ETDs, which is a relatively under-explored area. The result of the project will help to address the difficulty of information extraction from ETD documents, the potential of transfer learning on automatic summarization of ETD chapters, and the quality of state-of-the-art deep learning summarization technologies when applied to the ETD corpus.

The goal of this project is to generate chapter level abstractive summaries for an ETD collection through deep learning. Major challenges of the project include accurately extracting well-formatted chapter text from PDF files, and the lack of labeled data for supervised deep learning models. For PDF processing, we compare two state of the art scholarly PDF data extraction tools, Grobid and Science-Parse, which generate structured documents from which we can further extract metadata and chapter level text. For the second challenge, we perform transfer learning by training supervised learning models on a labeled dataset of Wikipedia articles related to the ETD collection. Our experimental models include Sequence-to-Sequence and Pointer Generator summarization models. Besides supervised models, we also experiment with an unsupervised reinforcement model, Fast Abstractive Summarization-RL.

The general pipeline for our experiments consists of the following steps: PDF data processing and chapter extraction, collecting a training data set of Wikipedia articles, manually creating human generated gold standard summaries for testing and validation, building deep learning models for chapter summarization, evaluating and tuning the models based on results, and then iteratively refining the whole process.

Contents

This repository is made up of several Python scripts and Jupyter notebooks organized in separate directories.

  • training data scripts – Scripts for managing and manipulating the training corpus

    • clean_wiki_text.py – Take out the wiki markup and only leave clean sentences.

    • doc2vec_similarity.ipynb – Use doc2vec to find similar wikipedia articles from ETD chapters

    • doc2vec_train_wiki_model.py – Train doc2vec on Wikipedia dump and save models

    • sentence_tokenization.py – Extract sentences from Grobid-generated TEI XML

    • wiki_summary_segmentation.py – Extract Wikipedia article summary given the page_name by hitting the Wikipedia API.

  • pipeline – Training data pipeline: scripts for extracting chapters from TEI, comparing and gathering similar Wikipedia articles, cleaning article text, and generating bin files for training

    • grobid_paragraph_extraction.py – Parse and transform GROBID TEI into JSON containing title, chapters, and paragraphs

    • Wiki_ETD_Similarity_Extraction.ipynb – Use doc2vec models to find Wikipedia articles similar to our corpus of ETDs

    • WikiJson_to_PGM_Bin.ipynb – Convert Wikipedia article JSON into PGM bin files

  • science parse

    • setup_science_parse.sh – Single script to completely set-up science-parse on remote server (with dependencies)

  • CNN bin to CSV – convert bin files to human-readable CSV

    • cnn_bin_file_to_csv_converter.py – convert the CNN dailymail dataset bin files into a simple CSV file which can also be fed to other networks

Usage

Where to Get Data

VTechWorks ETD collection

The majority of the ETDs housed in VTechWorks, the Virginia Tech institutional repository maintained by the University Libraries, are open access and can be viewed and downloaded free of charge.

Wikipedia Dump

Wikimedia foundation does regular Wikipedia Database backup dumps in different format. In our experiment, we use the latest pages and articles backup. Please find "enwiki-latest-pages-articles.xml.bz2" file from the link to latest English Wiki database backups. The data is in XML format and the schema can be found through Wikimedia dump XML schema. Notice that the XML schema defines the complete structure for all Wiki dumps, different version of backups may share a subset of the whole schema.

Links to official resources:

Gensim Wiki Parser

We use Gensim scripts for extracting plain text out of a raw Wikipedia dump. Use the following command to parse "enwiki-latest-pages-articles.xml.bz2":

    python -m gensim.scripts.segment_wiki -f enwiki-latest-pages-articles.xml.bz2 -o enwiki-latest.json.gz}

Command arguments:

  • -h show this help message and exit

  • -f path to Wiki dump (read-only).

  • -o path to output file (stdout if not specified). If ends in .gz or .bz2, the output file will be automatically compressed (recommended!).

  • -w number of parallel workers for multi-core systems. Default: 7.

  • -m ignore articles with fewer characters than this (article stubs). Default: 200.

Official Documentation:
https://radimrehurek.com/gensim/scripts/segment_wiki.html

CNN-Dailymail Dataset

CNN-Dailymail dataset is the original dataset used for our different deep learning summarizing models.

Links to official resources:

On the Github page, you will learn how to download and process the original original CNN-Dailymail data. You will also learn how to use the processed data directly.

In our experiments, we use the processed data provided by the official Github page. Our code for processing Wikipedia data into binary format also refers the code in this repository.

Grobid

Links to official resources:

Installation and Usage

There are various of ways to use Grobid: run locally as an application; run locally as a service; run locally through JAVA interface etc. Here, we use the JAR file to run Grobid through JAVA interface. Please refer official documentation for other usage.

  • Get the latest JAR release from “Latest Release” link

  • Clone the latest Github repo

  • Use following command to parse your PDF data:

        java -Xmx4G -jar  /path/to/grobid-core-0.5.2-SNAPSHOT-onejar.jar -ignoreAssets -r -gH /path/to/github/grobid/grobid-home -dIn /input/directory/ -dOut /output/directory/ -exe processFullText

Command arguments explanation:

  • -gH path to grobid-home directory

  • -dIn path to the directory of input PDF files

  • -dOut path to the output directory (if omitted the current directory)

  • -ignoreAssets do not extract and save the PDF assets (bitmaps, vector graphics), by default the assets are extracted and saved

  • -r recursive processing of files in the sub-directories (by default not recursive)

For other details, please check following manual page:
https://grobid.readthedocs.io/en/latest/Grobid-batch/

ScienceParse

Installation and Usage

There are three different ways to get started with Science Parse. Each has its own document:

  • Server: This contains the SP server. It’s useful for PDF parsing as a service. It’s also probably the easiest way to get going.

  • CLI: This contains the command line interface to SP. That’s most useful for batch processing.

  • Core: This contains SP as a library. It has all the extraction code, plus training and evaluation. Both server and CLI use this to do the actual work.

Alternatively, you can run the docker image:

    docker run -p 8080:8080 --rm allenai/scienceparse:2.0.1

We used the ‘cli’ version of science parse. Following are the steps to get it running:

  • Download SBT

        wget https://piccolo.link/sbt-1.2.4.zip

  • Unzip it

        unzip sbt-1.2.4.zip

  • Setup the path:

        export PATH=$PATH:~/sbt/bin

  • In Cascades, load the JDK module:

        module load jdk/1.8.0u172

  • Clone the Science Parse github repository

        git clone https://github.com/allenai/science-parse.git

  • Change the directory to the cloned repository

        cd science-parse

  • Start the build of the fat jar using SBT:

        sbt cli/assembly

  • After the build is complete, change the directory to the built jar file

        cd cli/target/scala-2.11

  • Create a test PDF file to download the pre-trained models:

        touch test.pdf

  • Trigger the parser for the first time using this test pdf file. It is ideal to get 6 GB of memory to load the pre-trained models:

        java -Xmx6g -jar science-parse-cli-assembly-2.0.2-SNAPSHOT.jar test.pdf || true

  • Removing the temporary

        rm test.pdf

  • Setup complete.

Use the following command to parse a single PDF file:

java -Xmx6g -jar /path/to/science-parse-cli-assembly-2.0.2-SNAPSHOT.jar input.pdf

Use the following command to parse multiple PDF files:

java -Xmx6g -jar /path/to/scala-2.11/science-parse-cli-assembly-2.0.2-SNAPSHOT.jar <path_to_the_folder_with_PDFs> -o <output_directory_path>

Use the following command to know more options:

java -Xmx6g -jar /path/to/scala-2.11/science-parse-cli-assembly-2.0.2-SNAPSHOT.jar --help

Pointer-Summarizer

Point-summarizer is the PyTorch implementation of pointer-generator.
Links to official resources:

Installation and Usage

  1. Clone the code from offical Github

  2. Set up environment for the program
    Option 1: Set up following environment in your preferred way:

    • Python 2.7

    • PyTorch 3.0

    • Tenserflow 1.10

    Option 2: Create environment from our provided Conda YML file [1]

  3. Find config.py file in data_util directory

  4. Configure parameters in config.py
    train_data_path: training data path
    eval_data_path: evaluation data path
    decode_data_path: decode data path
    vocab_path: vocabulary file path
    log_root: output and log path

  5. Run the program through following scripts:
    start_train.sh: run training
    start_eval.sh: run evaluation, provide model path as argument
    start_decode.sh: run decoding, provide model path as argument

Note:

  • For "config.py not found" error, add following code to according file:

        import sys
    sys.path.append('/path/to/pointer-summarizer')
  1. https://github.com/xw0078/CS5984Fall_Team16/tree/master/conda_yml

Seq2Seq Model

Links to official resources:

This model implements a simple sequence-to-sequence model with an Attentional Recurrent Neural Network (RNN) encoder-decoder.

Installation

This model can be used by cloning the latest code from the GitHub repository mentioned above. Once the code has been cloned, the model needs to be trained and then testing can be done by making use of the trained model.

Fast Abstractive Summarization-RL

The Github link[1] to the project provides a detailed information about the setup.

Prerequisites

  1. Create a conda environment and install the following dependencies:

    • Gensim

    • tensorboardX

    • Cytoolz

    • Pyrouge

  2. If you are working on Cascades, install the PyTorch Linux binaries compiled with CUDA 9.0[2]

  3. Clone the project from Github

Execution

  1. You can directly decode the pretrained model available in the repository or preprocess the CNN/Dailymail dataset by following the steps outlined[3]

  2. To train your model on the Wikipedia corpus, preprocess the dataset by running our code[4]

  3. Train a word2vec word embedding by running the following script:

        python train_word2vec.py --dim=300 --path=[word2vecPath]

    where dim parameter denotes the dimensionality (default value is 128) and path denotes the path to save the word2vec model.

  4. Make the pseudo-labels by running the command:

        python make_extraction_labels.py

    This will create labels in the training and validation dataset and add the arrays score and extracted to them.

  5. Train the abstractor and extractor using ML objectives:

        python train_abstractor.py --path=[path/toSave/abstractor/model] --w2v=[path/to/word2vec.bin]

  6. Train the RL model

         python train_full_rl.py --path=[path/to/save/model] --abs_dir=[path/to/abstractor/model] --ext_dir=[path/to/extractor/model]

  7. Decode the model by running:

         python decode_full_model.py --path=[path/to/save/decoded/files] --model_dir=[path/to/pretrainedRL] --beam= 5 --test

  1. https://github.com/ChenRocks/fast_abs_rl

  2. https://pytorch.org/get-started/previous-versions/##pytorch-linux-binaries-compiled-with-cuda-90

  3. https://github.com/ChenRocks/cnn-dailymail

  4. https://github.com/namanahuja/CS5984Fall_Team16/tree/master/FastRL_PreProcessWikiData

Credits

Naman Ahuja (@namanahuja)
Ritesh Bansal (@Riteshbansal)
Bill Ingram (@waingram)
Palakh Jude (@palakhjude)
Sampanna Kahu (@sampannakahu)
Xinyue Wang (@xw0078)