Extreme Extractive Text Summarization and Topic Modeling over Reddit Posts from TLDRHQ dataset.

Table of contents

• Abstract
• Paper and Slides
• Requirements
• TLDRHQ: Data and Text Pre-processing
• Extreme Extractive Text Summarization
• Topic Modeling
• Status
• Contact
• License
• Contributing

Abstract

Reddit is a social news aggregation and discussion website where users post content (such as links, text posts, images, and videos) on a wide range of topics in domain-specific boards called ”communities” or ”subreddits.” The following project aims to implement text summarization and topic modelling pipelines on the textual content of Redditors’ posts. The dataset used to achieve these goals is the Reddit-based TL;DR summarization dataset TLDRHQ containing approximately 1.7 million Reddit posts (submissions as well as comments) obtained through scraping techniques. Each instance in the dataset includes a Reddit post and its TL;DR, which is an acronym for ”Too Long; Didn’t Read” and is an extremely short summary of the post’s content that is good practice for users to leave at the end of a post. While the Reddit usage has increased, the practice of write TL;DR didn’t keep the pace. In this context, a system (such as a bot) capable to automatically generate the TL;DR of a post could improve Reddit usability. However, the high abstractivity, heterogeneity and noisy of posts make the text summarization task challenging. In this work a supervised extreme extractive summarization model is developed. Despite its lower complexity, results show that its performance are not so different with respect to the state of the art BertSumExt. Moreover the topic modeling analysis of the posts could be really useful in identifying the hidden topics in a post and evaluate if it’s published in the right subreddit. In this project LSA and LDA techniques are used. On this dataset LSA outperformed LDA and identified 20 well defined topics providing the respective document-topics and topic-terms matrices

Requirements

• python 3.10.7
• contractions==0.1.73
• gensim==4.3.0
• ipython==8.8.0
• matplotlib==3.6.3
• nltk==3.8.1
• num2words==0.5.12
• numpy==1.22.1
• pandas==1.3.5
• seaborn==0.12.2
• simplemma==0.9.0
• spacy==3.4.4
• swifter==1.3.4
• textblob==0.17.1
• tqdm==4.64.1
• scikit-learn==1.2.0
• rouge-score==0.1.2
• imbalanced-learn==0.10.1
• wordcloud==1.8.2.2
• pyLDAvis==3.3.1

TLDRHQ: Data and Text Pre-processing

Step 0. Prepare Folders

First of all, create three empty folders: ./DatasetTLDRHQ,./ProcessedData and ./Dataset_splitted.

Step 1. Download and extract the dataset

Download annotations from the official Google Drive Folder and extract them in ./DatasetTLDRHQ, resulting in a folder tree like this:

project_folder
└───Dataset_TLDRHQ
    ├───dataset-m0
    ├───dataset-m1
    ├───dataset-m2
    ├───dataset-m2021
    ├───dataset-m3
    ├───dataset-m4
    └───dataset-m6

Step 2. Perform data cleaning and splitting of the dataset

Run the 0_cleaning.py script which will perform data cleaning (removing duplicates), splits the dataset into training/validation and test sets (splitting the training set so that it is easier to manage) and then save it splitted into .JSON files in ./Dataset_splitted. You get a directory tree like this:

project_folder
└───Dataset_splitted
    ├───test.json
    ├───train_1.json
    ├───train_10.json
    ├───train_2.json
    ├───train_3.json
    ├───train_4.json
    ├───train_5.json
    ├───train_6.json
    ├───train_7.json
    ├───train_8.json
    ├───train_9.json
    └───val.json

Step 3. Perform text pre-processing on the dataset

Run the 0_normalizing.py script which will perform senteces splitting, text normalization, tokenization, stop-words removal, lemmatization and POS tagging on document variable, containing reddit posts. Then save it splitted into various .JSON files in ./ProcessedData. You get a directory tree like this:

project_folder
└───ProcessedData
    ├───test.json
    ├───train_1.json
    ├───train_10.json
    ├───train_2.json
    ├───train_3.json
    ├───train_4.json
    ├───train_5.json
    ├───train_6.json
    ├───train_7.json
    ├───train_8.json
    ├───train_9.json
    └───val.json

The text normalisation operations performed include, in order: Sentence Splitting, HTML tags and entities removal, Extra White spaces Removal, URLs Removal, Emoji Removal, User Age Processing (e.g. 25m becomes 25 male), Numbers Processing, Control Characters Removal, Case Folding, Repeated characters processing (e.g. reallllly becomes really), Fix and Expand English contradictions, Special Characters and Punctuation Removal, Tokenization (Uni-Grams), Stop-Words and 1-character tokens, Lemmatization and POS tagging.

Extreme Extractive Summarization task

Step 0. Split and clean 'ProcessedData' for easy management

Run notebook 1_preprocessing4summarization.ipynb in order to:

• remove document without summary
• remove document with a single sentence
• split train dataset

project_folder
└───Processed Data For Summarization
    ├───test_0.json
    ├───test_1.json
    ├───test_2.json
    ├───train_1_0.json
    ├───train_1_1.json
    ├───train_1_2.json
    ├───train_2_0.json
    ├───train_2_1.json
    ├───train_2_2.json
    ├───  ...
    ├───train_8_0.json
    ├───train_8_1.json
    ├───train_8_2.json
    ├───train_9_0.json
    ├───val_0.json
    ├───val_1.json
    └───val_2.json

Step 1. Create a feature matrix for each of the JSON in 'Processed Data For Summarization'

Run 1_featureMatrixGeneration.py obtaining feature matrices (sentences x features). You get a directory tree like this:

project_folder
└───Feature Matrices
    ├───test_0.csv
    ├───test_1.csv
    ├───test_2.csv
    ├───train_1_0.csv
    ├───train_1_1.csv
    ├───train_1_2.csv
    ├───train_2_0.csv
    ├───train_2_1.csv
    ├───train_2_2.csv
    ├───  ...
    ├───train_8_0.csv
    ├───train_8_1.csv
    ├───train_8_2.csv
    ├───train_9_0.csv
    ├───val_0.csv
    ├───val_1.csv
    └───val_2.csv

Run the notebook 1_featureMatrixGeneration2.ipynb to join train, val and test datasets. You get a directory tree like this:

project_folder
└───Feature Matrices
   ├───test_0.csv
   ├───test_1.csv
   ├───test_2.csv
   ├───train_1_0.csv
   ├───train_1_1.csv
   ├───train_1_2.csv
   ├───train_2_0.csv
   ├───train_2_1.csv
   ├───train_2_2.csv
   ├───  ...
   ├───train_8_0.csv
   ├───train_8_1.csv
   ├───train_8_2.csv
   ├───train_9_0.csv
   ├───val_0.csv
   ├───val_1.csv
   ├───val_2.csv
   ├───test.csv
   ├───train.csv
   └───val.csv

Features generated at this step are the following:

• sentence_relative_positions
• sentence_similarity_score_1_gram
• word_in_sentence_relative
• NOUN_tag_ratio
• VERB_tag_ratio
• ADJ_tag_ratio
• ADV_tag_ratio
• TF_ISF

Step 2. Perform CUR undersampling

Run notebook 1_featureMatrixUndersampling.ipynb in order to perform CUR undersampling on both train and validation data sets. You get a directory tree like this:

project_folder
└───Undersampled Data
    ├───trainAndValMinorityClass.csv
    └───trainAndValMajorityClassUndersampled.csv

Majority and minority class are splitted because CUR undersampling works only on the majority class

Step 3. Perform EditedNearestNeighbours(ENN) undersamplig

Run notebook 1_featureMatrixAnalysis.ipynb to perform EEN undersampling. You get a directory tree like this:

project_folder
└───Undersampled Data
    ├───trainAndValUndersampledENN3.csv
    ├───trainAndValMinorityClass.csv
    └───trainAndValMajorityClassUndersampled.csv

Step 4. Machine Learning model selection and evaluation

Run notebook 1_featureMatrixAnalysis.ipynb to perform a RandomizedSearcCV over the following models

• RandomForestClassifier
• LogisticRegression
• HistGradientBoostingClassifier

with a few possible parameters configuration.

Then, evaluate the resulting best model on the test set with respect to:

• ROC curve
• Recall
• Precision
• Accuracy

Step 5. Perform Maximal Marginal Relevance(MMR) selection

Run notebook 1_featureMatrixAnalysis.ipynb to perform MMR and obtain an extractive summary for each document in the test set.

Step 6. Summary Evaluation

Run notebook 1_featureMatrixAnalysis.ipynb to measure summaries quality by means of

• Rouge1
• Rouge2
• RougeL

Topic Modeling task

Step 0. Perform preprocessing

Run the 2_preprocessing4topic_modeling.ipynb script to process and extract only the useful data. The output is saved here:

project_folder
└───processed_dataset
    ├───test.json

Step 1. Perform topic modeling on the test set

Run the 2_topic_modeling.ipynb script which will perform LDA (with grid search of the best hyper-parameters) and LSA. The script saves 9 CSV files, 3 for LSA and 6 for LDA (UMass and CV coherence measures), containing: document-topic matrix, topic-term matrix and a table with topic insights.

project_folder
└───Results_topic_modeling
    ├───lda_doc_topic.csv
    ├───lda_doc_topic_CV.csv
    ├───lda_top_terms.csv
    ├───lda_top_terms_CV.csv
    ├───lda_topic_term.csv
    ├───lda_topic_term_cv.csv
    ├───lsa_doc_topic.csv
    ├───lsa_top_terms.csv
    ├───lsa_topic_term.csv

Saves images regarding the number of words per document and wordcloud in

project_folder
└───Images

Saves hyperparameters grid search results for UMass and CV coherence in

project_folder
└───Hyperparameters
    ├───tuning.csv
    ├───tuning_CV.csv

Status

Project is: Done

Contact

Giorgio Carbone - feel free to contact me!

License

• You can check out the full license here

This project is licensed under the terms of the MIT license.

Contributing

1. Fork it (https://github.com/giocoal/reddit-tldr-summarizer-and-topic-modeling.git)
2. Create your feature branch (git checkout -b feature/fooBar)
3. Commit your changes (git commit -am 'Add some fooBar')
4. Push to the branch (git push origin feature/fooBar)
5. Create a new Pull Request

Contributors

• Giorgio Carbone
• Marco Scatassi
• Gianluca Scuri