pysummarization is Python library for the automatic summarization, document abstraction, and text filtering in relation to Encoder/Decoder based on LSTM and LSTM-RTRBM.

# Automatic Summarization Library: pysummarization

pysummarization is Python3 library for the automatic summarization, document abstraction, and text filtering.

## Description

The function of this library is automatic summarization using a kind of natural language processing. This library enable you to create a summary with the major points of the original document or web-scraped text that filtered by text clustering. And this library applies pydbm to implement Encoder/Decoder based on LSTM and LSTM-RTRBM, improving the accuracy of summarization.

## Documentation

Full documentation is available on https://code.accel-brain.com/Automatic-Summarization/ . This document contains information on functionally reusability, functional scalability and functional extensibility.

## Installation

Install using pip:

pip install pysummarization

### Source code

The source code is currently hosted on GitHub.

### Python package index(PyPI)

Installers for the latest released version are available at the Python package index.

### Dependencies

• numpy: v1.13.3 or higher.
• nltk: v3.2.3 or higher.

#### Options

• mecab-python3: v0.7 or higher.
• Relevant only for Japanese.
• pdfminer2
• Relevant only for PDF files.
• pyquery:v1.2.17 or higher.
• Relevant only for web scraiping.
• pydbm: v1.3.2 or higher.
• Only when using Encoder/Decoder based on LSTM and LSTM-RTRBM.

## Usecase: Summarize an English string argument.

Import Python modules.

from pysummarization.nlpbase.auto_abstractor import AutoAbstractor
from pysummarization.tokenizabledoc.simple_tokenizer import SimpleTokenizer
from pysummarization.abstractabledoc.top_n_rank_abstractor import TopNRankAbstractor

Prepare an English string argument.

document = "Natural language generation (NLG) is the natural language processing task of generating natural language from a machine representation system such as a knowledge base or a logical form. Psycholinguists prefer the term language production when such formal representations are interpreted as models for mental representations."

And instantiate objects and call the method.

# Object of automatic summarization.
auto_abstractor = AutoAbstractor()
# Set tokenizer.
auto_abstractor.tokenizable_doc = SimpleTokenizer()
# Set delimiter for making a list of sentence.
auto_abstractor.delimiter_list = [".", "\n"]
# Object of abstracting and filtering document.
abstractable_doc = TopNRankAbstractor()
# Summarize document.
result_dict = auto_abstractor.summarize(document, abstractable_doc)

# Output result.
for sentence in result_dict["summarize_result"]:
print(sentence)

The result_dict is a dict. this format is as follows.

dict{
"summarize_result": "The list of summarized sentences.",
"scoring_data":     "The list of scores(Rank of importance)."
}

## Usecase: Summarize Japanese string argument.

Import Python modules.

from pysummarization.nlpbase.auto_abstractor import AutoAbstractor
from pysummarization.tokenizabledoc.mecab_tokenizer import MeCabTokenizer
from pysummarization.abstractabledoc.top_n_rank_abstractor import TopNRankAbstractor

Prepare an English string argument.

document = "自然言語処理（しぜんげんごしょり、英語: natural language processing、略称：NLP）は、人間が日常的に使っている自然言語をコンピュータに処理させる一連の技術であり、人工知能と言語学の一分野である。「計算言語学」（computational linguistics）との類似もあるが、自然言語処理は工学的な視点からの言語処理をさすのに対して、計算言語学は言語学的視点を重視する手法をさす事が多い[1]。データベース内の情報を自然言語に変換したり、自然言語の文章をより形式的な（コンピュータが理解しやすい）表現に変換するといった処理が含まれる。応用例としては予測変換、IMEなどの文字変換が挙げられる。"

And instantiate objects and call the method.

# Object of automatic summarization.
auto_abstractor = AutoAbstractor()
# Set tokenizer for Japanese.
auto_abstractor.tokenizable_doc = MeCabTokenizer()
# Set delimiter for making a list of sentence.
auto_abstractor.delimiter_list = ["。", "\n"]
# Object of abstracting and filtering document.
abstractable_doc = TopNRankAbstractor()
# Summarize document.
result_dict = auto_abstractor.summarize(document, abstractable_doc)

# Output result.
for sentence in result_dict["summarize_result"]:
print(sentence)

## Usecase: English Web-Page Summarization

Run the batch program: demo/demo_summarization_english_web_page.py

python demo/demo_summarization_english_web_page.py {URL}
• {URL}: web site URL.

### Demo

python demo/demo_summarization_english_web_page.py https://en.wikipedia.org/wiki/Natural_language_generation

The result is as follows.

Psycholinguists prefer the term language production when such formal representations are interpreted as models for mental representations.

It could be said an NLG system is like a translator that converts data into a natural language representation.

## Usecase: Japanese Web-Page Summarization

Run the batch program: demo/demo_summarization_japanese_web_page.py

python demo/demo_summarization_japanese_web_page.py {URL}
• {URL}: web site URL.

### Demo

python demo/demo_summarization_japanese_web_page.py https://ja.wikipedia.org/wiki/%E8%87%AA%E5%8B%95%E8%A6%81%E7%B4%84

The result is as follows.

自動要約 （じどうようやく）は、 コンピュータプログラム を用いて、文書からその要約を作成する処理である。

単一文書要約と複数文書要約 [ 編集 ] 単一文書要約 は、単一の文書を要約の対象とするものである。

## Usecase: Japanese Web-Page Summarization with N-gram

The minimum unit of token is not necessarily a word in automatic summarization. N-gram is also applicable to the tokenization.

Run the batch program: demo/demo_with_n_gram_japanese_web_page.py

python demo_with_n_gram_japanese_web_page.py {URL}
• {URL}: web site URL.

### Demo

python demo/demo_with_n_gram_japanese_web_page.py https://ja.wikipedia.org/wiki/%E6%83%85%E5%A0%B1%E6%A4%9C%E7%B4%A2

The result is as follows.

パターンマッチング 検索質問として入力された表現をそのまま含む文書を検索するアルゴリズム。

ベクトル空間モデル キーワード等を各 次元 として設定した高次元 ベクトル空間 を想定し、検索の対象とするデータやユーザによる検索質問に何らかの加工を行い ベクトル を生成する

## Usecase: Summarization, filtering the mutually similar, tautological, pleonastic, or redundant sentences

If the sentences you want to summarize consist of repetition of same or similar sense in different words, the summary results may also be redundant. Then before summarization, you should filter the mutually similar, tautological, pleonastic, or redundant sentences to extract features having an information quantity. The function of SimilarityFilter is to cut-off the sentences having the state of resembling or being alike by calculating the similarity measure.

But there is no reason to stick to a single similarity concept. Modal logically, the definition of this concept is contingent, like the concept of distance. Even if one similarity or distance function is defined in relation to a problem setting, there are always functionally equivalent algorithms to solve the problem setting. Then this library has a wide variety of subtyping polymorphisms of SimilarityFilter.

### Dice, Jaccard, and Simpson

There are some classes for calculating the similarity measure. In this library, Dice coefficient, Jaccard coefficient, and Simpson coefficient between two sentences is calculated as follows.

Import Python modules for calculating the similarity measure and instantiate the object.

from pysummarization.similarityfilter.dice import Dice
similarity_filter = Dice()

or

from pysummarization.similarityfilter.jaccard import Jaccard
similarity_filter = Jaccard()

or

from pysummarization.similarityfilter.simpson import Simpson
similarity_filter = Simpson()

### Functional equivalent: Combination of Tf-Idf and Cosine similarity

If you want to calculate similarity with Tf-Idf cosine similarity, instantiate TfIdfCosine.

from pysummarization.similarityfilter.tfidf_cosine import TfIdfCosine
similarity_filter = TfIdfCosine()

### Functional equivalent: Combination of Encoder/Decoder based on LSTM and Cosine similarity

According to the neural networks theory, and in relation to manifold hypothesis, it is well known that multilayer neural networks can learn features of observed data points and have the feature points in hidden layer. High-dimensional data can be converted to low-dimensional codes by training the model such as Stacked Auto-Encoder and Encoder/Decoder with a small central layer to reconstruct high-dimensional input vectors. This function of dimensionality reduction facilitates feature expressions to calculate similarity of each data point.

This library provides Encoder/Decoder based on LSTM, which makes it possible to extract series features of natural sentences embedded in deeper layers. Intuitively speaking, similarities of the series feature points correspond to similarities of the observed data points. You can extracted the result of dimensionality reduction and cosine similarity of the manifolds, which is embedded in hidden layer of Encoder/Decoder based on LSTM, by coding as follows.

from pysummarization.similarityfilter.encoder_decoder_cosine import EncoderDecoderCosine

# Instantiation and learn natural sentences.
similarity_filter = EncoderDecoderCosine(
# String of natural sentences.
document,
# The number of hidden units.
hidden_neuron_count=200,
# Epochs of Mini-batch.
epochs=100,
# Batch size of Mini-batch.
batch_size=100,
# Learning rate.
learning_rate=1e-05,
# Attenuate the learning_rate by a factor of this value every attenuate_epoch.
learning_attenuate_rate=0.1,
# Attenuate the learning_rate by a factor of learning_attenuate_rate every attenuate_epoch.
attenuate_epoch=50,
# Refereed maxinum step t in Backpropagation Through Time(BPTT).
bptt_tau=8,
# Regularization for weights matrix
# to repeat multiplying the weights matrix and 0.9
# until $\sum_{j=0}^{n}w_{ji}^2 < weight\_limit$.
weight_limit=0.5,
# The probability of dropout.
dropout_rate=0.5,
# Size of Test data set. If this value is 0, the validation will not be executed.
test_size_rate=0.3,
# Debug mode or not.
debug_mode=True
)

document is a str of all natural sentences, which are subject to automatic summarization by AutoAbstractor. When instantiated, EncoderDecoderCosine converts the datasets to t-hot vectors of each token, of which the shape is (The number of sentences, The mean number of token, The dimention of t-hot), and starts learning. If debug_mode is True, the progress of learning is printed by the logger.

Refer to pydbm library for details related to Encoder/Decoder.

### Functional equivalent: Combination of LSTM-RTRBM and Cosine similarity

The methodology of equivalent-functionalism enables us to introduce more functional equivalents and compare problem solutions structured with different algorithms and models in common problem setting. For example, in dimension reduction problem for temporal or sequencial patterns, the function of LSTM-RTRBM is equivalent to Encoder/Decoder based on LSTM.

LSTM-RTRBM model integrates the ability of LSTM in memorizing and retrieving useful history information, together with the advantage of RBM in high dimensional data modelling. LSTM-RTRBM is a probabilistic time-series model which can be viewed as a temporal stack of RBMs, where each RBM has a contextual hidden state that is received from the previous RBM and is used to modulate its hidden units bias. This model can learn dependency structures in temporal patterns such as music, natural sentences, and n-gram.

This library provides LSTM-RTRBM, which makes it possible to extract series features points of natural sentences. You can also extracted the result of dimensionality reduction and cosine similarity of the manifolds, which is embedded in hidden layer of LSTM-RTRBM, by coding as follows.

from pysummarization.similarityfilter.lstm_rtrbm_cosine import LSTMRTRBMCosine

similarity_filter = LSTMRTRBMCosine(
# String of natural sentences.
document,
# The number of hidden units.
hidden_neuron_count=1000,
# Batch size of Mini-batch.
batch_size=10,
# Learning rate.
learning_rate=1e-03,
# The length of one sequence.
seq_len=5,
# Debug mode or not.
debug_mode=True
)

Refer to pydbm library for details related to LSTM-RTRBM.

### Functional equivalent: Data clustering

It is not inevitable to grasp the concept of similarity as cosine similairty. This library makes it possible to adopt k-means clustering to find similar sentences from the feature points which is generated by Encoder/Decoder based on LSTM or LSTM-RTRBM.

The similarity is observed by checking whether each sentence belonging to the same cluster, and if so, the similarity is 1.0, if not, the value is 0.0. The data clustering algorithm is based on K-Means method, learning data which is embedded in hidden layer of LSTM or LSTM-RTRBM.

In this library, if two arbitrarily selected sentences belong to the same cluster, the sentences is considered as the mutually similar, tautological, pleonastic, or redundant sentences.

from pysummarization.similarityfilter.encoder_decoder_clustering import EncoderDecoderClustering

# Instantiation and learn natural sentences.
similarity_filter = EncoderDecoderClustering(
# String of natural sentences.
document,
# The number of hidden units.
hidden_neuron_count=200,
# Epochs of Mini-batch.
epochs=100,
# Batch size of Mini-batch.
batch_size=100,
# Learning rate.
learning_rate=1e-05,
# Attenuate the learning_rate by a factor of this value every attenuate_epoch.
learning_attenuate_rate=0.1,
# Attenuate the learning_rate by a factor of learning_attenuate_rate every attenuate_epoch.
attenuate_epoch=50,
# Refereed maxinum step t in Backpropagation Through Time(BPTT).
bptt_tau=8,
# Regularization for weights matrix
# to repeat multiplying the weights matrix and 0.9
# until $\sum_{j=0}^{n}w_{ji}^2 < weight\_limit$.
weight_limit=0.5,
# The probability of dropout.
dropout_rate=0.5,
# Size of Test data set. If this value is 0, the validation will not be executed.
test_size_rate=0.3,
# The number of clusters.
cluster_num=20,
# Maximum number of iterations.
max_iter=100,
# Debug mode or not.
debug_mode=True
)

from pysummarization.similarityfilter.lstm_rtrbm_clustering import LSTMRTRBMClustering

similarity_filter = LSTMRTRBMClustering(
# String of natural sentences.
document,
# The number of hidden units.
hidden_neuron_count=1000,
# Batch size.
batch_size=100,
# Learning rate.
learning_rate=1e-03,
# The length of one sequence observed by LSTM-RTRBM.
seq_len=5,
# The number of clusters.
cluster_num=cluster_num,
# Maximum number of iterations.
max_iter=100,
# Debug mode or not.
debug_mode=True
)

### Calculating similarity

If you want to calculate similarity between two sentences, call calculate method as follow.

# Tokenized sentences
token_list_x = ["Dice", "coefficient", "is", "a", "similarity", "measure", "."]
token_list_y = ["Jaccard", "coefficient", "is", "a", "similarity", "measure", "."]
# 0.75
similarity_num = similarity_filter.calculate(token_list_x, token_list_y)

### Filtering similar sentences and summarization

The function of these methods is to cut-off mutually similar sentences. In text summarization, basic usage of this function is as follow. After all, SimilarityFilter is delegated as well as GoF's Strategy Pattern.

Import Python modules for NLP and text summarization.

from pysummarization.nlp_base import NlpBase
from pysummarization.nlpbase.auto_abstractor import AutoAbstractor
from pysummarization.tokenizabledoc.mecab_tokenizer import MeCabTokenizer
from pysummarization.abstractabledoc.top_n_rank_abstractor import TopNRankAbstractor
from pysummarization.similarityfilter.tfidf_cosine import TfIdfCosine

Instantiate object of the NLP.

# The object of the NLP.
nlp_base = NlpBase()
# Set tokenizer. This is japanese tokenizer with MeCab.
nlp_base.tokenizable_doc = MeCabTokenizer()

Instantiate object of SimilarityFilter and set the cut-off threshold.

# The object of Similarity Filter.
# The similarity observed by this object is so-called cosine similarity of Tf-Idf vectors.
similarity_filter = TfIdfCosine()

# Set the object of NLP.
similarity_filter.nlp_base = nlp_base

# If the similarity exceeds this value, the sentence will be cut off.
similarity_filter.similarity_limit = 0.25

Prepare sentences you want to summarize.

# Summarized sentences (sited from http://ja.uncyclopedia.info/wiki/%E5%86%97%E8%AA%9E%E6%B3%95).
document = "冗語法（じょうごほう、レデュンダンシー、redundancy、jōgohō）とは、何度も何度も繰り返し重ねて重複して前述されたのと同じ意味の同様である同意義の文章を、必要あるいは説明か理解を要求された以上か、伝え伝達したいと意図された、あるいは表し表現したい意味以上に、繰り返し重ねて重複して繰り返すことによる、不必要であるか、または余分な余計である文章の、必要以上の使用であり、何度も何度も繰り返し重ねて重複して前述されたのと同じ意味の同様の文章を、必要あるいは説明か理解を要求された以上か、伝え伝達したいと意図された、あるいは表し表現したい意味以上に、繰り返し重ねて重複して繰り返すことによる、不必要であるか、または余分な文章の、必要以上の使用である。これが冗語法（じょうごほう、レデュンダンシー、redundancy、jōgohō）である。基本的に、冗語法（じょうごほう、レデュンダンシー、redundancy、jōgohō）が多くの場合において概して一般的に繰り返される通常の場合は、普通、同じ同様の発想や思考や概念や物事を表し表現する別々の異なった文章や単語や言葉が何回も何度も余分に繰り返され、その結果として発言者の考えが何回も何度も言い直され、事実上、実際に同じ同様の発言が何回も何度にもわたり、幾重にも言い換えられ、かつ、同じことが何回も何度も繰り返し重複して過剰に回数を重ね前述されたのと同じ意味の同様の文章が何度も何度も不必要に繰り返される。通常の場合、多くの場合において概して一般的にこのように冗語法（じょうごほう、レデュンダンシー、redundancy、jōgohō）が繰り返される。"

Instantiate object of AutoAbstractor and call the method.

# The object of automatic sumamrization.
auto_abstractor = AutoAbstractor()
# Set tokenizer. This is japanese tokenizer with MeCab.
auto_abstractor.tokenizable_doc = MeCabTokenizer()
# Object of abstracting and filtering document.
abstractable_doc = TopNRankAbstractor()
# Delegate the objects and execute summarization.
result_dict = auto_abstractor.summarize(document, abstractable_doc, similarity_filter)

### Demo

Run the batch program: demo/demo_similarity_filtering_japanese_web_page.py

python demo/demo_similarity_filtering_japanese_web_page.py {URL} {SimilarityFilter} {SimilarityLimit}
• {URL}: web site URL.
• {SimilarityFilter}: The object of SimilarityFilter:
• Dice
• Jaccard
• Simpson
• TfIdfCosine
• EncoderDecoderCosine
• EncoderDecoderClustering
• LSTMRTRBMCosine
• LSTMRTRBMClustering
• {SimilarityLimit}: The cut-off threshold.

For instance, command line argument is as follows:

python demo/demo_similarity_filtering_japanese_web_page.py https://ja.wikipedia.org/wiki/%E5%BE%AA%E7%92%B0%E8%AB%96%E6%B3%95 Jaccard 0.3

The result is as follows.

ある用語の 定義 を与える表現の中にその用語自体が本質的に登場していること [1]

# References

• Boulanger-Lewandowski, N., Bengio, Y., & Vincent, P. (2012). Modeling temporal dependencies in high-dimensional sequences: Application to polyphonic music generation and transcription. arXiv preprint arXiv:1206.6392.
• Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.
• Luhn, Hans Peter. "The automatic creation of literature abstracts." IBM Journal of research and development 2.2 (1958): 159-165.
• Lyu, Q., Wu, Z., Zhu, J., & Meng, H. (2015, June). Modelling High-Dimensional Sequences with LSTM-RTRBM: Application to Polyphonic Music Generation. In IJCAI (pp. 4138-4139).
• Lyu, Q., Wu, Z., & Zhu, J. (2015, October). Polyphonic music modelling with LSTM-RTRBM. In Proceedings of the 23rd ACM international conference on Multimedia (pp. 991-994). ACM.
• Matthew A. Russell　著、佐藤 敏紀、瀬戸口 光宏、原川 浩一　監訳、長尾 高弘　訳『入門 ソーシャルデータ 第2版――ソーシャルウェブのデータマイニング』 2014年06月 発行
• Sutskever, I., Hinton, G. E., & Taylor, G. W. (2009). The recurrent temporal restricted boltzmann machine. In Advances in Neural Information Processing Systems (pp. 1601-1608).

## Author

• chimera0(RUM)

## Author URI

• GNU General Public License v2.0