Summarizing Text Summarization

There are many tasks in natural language processing that are challenging. This blog entry is on text summarization, which briefly summarizes the survey article on this topic. (arXiv:1707.02268) The authors of the article defined the task to be

Automatic text summarization is the task of producing a concise and fluent summary while preserving key information content and overall meaning.

There are basically two approaches to this task:

  • extractive summarization: identifying important sections of the text, and extracting them; and
  • abstractive summarization: producing summary text in a new way.

Most algorithmic methods developed are of the extractive type, while most human writers summarize using abstractive approach. There are many methods in extractive approach, such as identifying given keywords, identifying sentences similar to the title, or wrangling the text at the beginning of the documents.

How do we instruct the machines to perform extractive summarization? The authors mentioned about two representations: topic and indicator. In topic representations, frequencies, tf-idf, latent semantic indexing (LSI), or topic models (such as latent Dirichlet allocation, LDA) are used. However, simply extracting these sentences out with these algorithms may not generate a readable summary. Employment of knowledge bases or considering contexts (from web search, e-mail conversation threads, scientific articles, author styles etc.) are useful.

In indicator representation, the authors mentioned the graph methods, inspired by PageRank. (see this) “Sentences form vertices of the graph and edges between the sentences indicate how similar the two sentences are.” And the key sentences are identified with ranking algorithms. Of course, machine learning methods can be used too.

Evaluation on the performance on text summarization is difficult. Human evaluation is unavoidable, but with manual approaches, some statistics can be calculated, such as ROUGE.

Continue reading “Summarizing Text Summarization”

Essential Python Packages

Almost three years ago, I wrote a blog entry titled Useful Python Packages, which listed the essential packages that I deemed important. How has the list been changed over the past three years?

First of all, three years ago, most people were still writing Python 2.7. But now there is a trend to switch to Python 3. I admitted that I still have not started the switch yet, but in the short term, I will have no choice and I will.

What are some of the essential packages?
Numerical Packages

  • numpy: numerical Python, containing most basic numerical routines such as matrix manipulation, linear algebra, random sampling, numerical integration etc. There is a built-in wrapper for Fortran as well. Actually, numpy is so important that some Linux system includes it with Python.
  • scipy: scientific Python, containing some functions useful for scientific computing, such as sparse matrices, numerical differential equations, advanced linear algebra, special functions etc.
  • networkx: package that handles various types of networks
  • PuLP: linear programming
  • cvxopt: convex optimization

Data Visualization

  • matplotlib: basic plotting.
  • ggplot2: the ggplot2 counterpart in Python for producing quality publication plots.

Data Manipulation

  • pandas: data manipulation, working with data frames in Python, and save/load of various formats such as CSV and Excel

Machine Learning

  • scikit-learn: machine-learning library in Python, containing classes and functions for supervised and unsupervised learning

Probabilistic Programming

  • PyMC: Metropolis-Hasting algorithm
  • Edward: deep probabilistic programing

Deep Learning Frameworks

  • TensorFlow: because of Google’s marketing effort, TensorFlow is now the industrial standard for building deep learning networks, with rich source of mathematical functions, esp. for neural network cells, with GPU capability
  • Keras: containing routines of high-level layers for deep learning neural networks, with TensorFlow, Theano, or CNTK as the backbone
  • PyTorch: a rivalry against TensorFlow

Natural Language Processing

  • nltk: natural language processing toolkit for Python, containing bag-of-words model, tokenizer, stemmers, chunker, lemmatizers, part-of-speech taggers etc.
  • gensim: a useful natural language processing package useful for topic modeling, word-embedding, latent semantic indexing etc., running in a fast fashion
  • shorttext: text mining package good for handling short sentences, that provide high-level routines for training neural network classifiers, or generating feature represented by topic models or autoencodings.
  • spacy: industrial standard for natural language processing common tools

GUI

I can probably list more, but I think I covered most of them. If you do not find something useful, it is probably time for you to write a brand new package.

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