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Questions tagged [text-summarization]

Text summarization is the process of reducing a text document in order to create a summary that retains the most important points of the original document.

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Architectures for Text Genre Classification

I am currently trying to build a model for giving genres to news articles. I was wondering what kind of architectures would be good to use for such a task? I am pretty unfamiliar with the current ...
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Whats the difference and similarity between topic modeling (LDA) and text summarization (textrank)

The concept of these two (Topic modeling and Text summarization) are similar because Topic modeling gives you important number of topics and summarization gives you important summary of a large text. ...
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Associate keyword from textrank algorithm in R to verbatim [closed]

I have created a frequency table using TextRank algorithm in R. While, I get the frequencies, I want to associate back the text to the verbatim in the form of a matrix so that I know from where each ...
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Representation for meaning of a text document

Does anyone know a technique to represent meaning of a given text document, so that two documents having same meaning will have same representations? Note: Typical systems like vectorization methods ...
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Question related to interpretation of Empirical CDF [closed]

Following is the results from my experiments. Pink and Green are my proposed algorithms. While Blue is the brute-force (BF) algorithm. While one thing is obvious that BF algorithms take a lot of time ...
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1answer
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Unsupervised answering for a predefined set of questions

I am working on a project to read up a text segment and find answers to a specific set of questions, in order to do some information extraction. I have a set of text corpus (each of about 3000 words),...
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Using type-2-fuzzy sets and systems for automatic document summarization

I have already done a research project based on automatic document summarization using deep learning techniques. But fuzzy logic is pretty new to me, and I don't have any experience of building system ...
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Considerations in applying machine learning / AI to “coding” of text responses

A client is looking at applying AI/ML to a dataset of textual responses for the purpose of: a) extracting one or more concepts or meanings from each response, and b) cross-referencing with other ...
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What kind of Text Analytics techniques I can use in this case?

I have product title data to extract a product category as a feature to train a model. The title examples are like: ...
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What other approaches are there for abstractive summarization, other then seq2seq?

I'm researching on abstractive text summarization, and has come across many recent papers. They all seem to be focusing on Sequence to Sequence models based on RNNs. Apart from RNNs, what other ...
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Do supervised methods outperform unsupervised methods for generic multi-document summarization of news?

{1} says: For generic multi-document summarization of news, supervised methods have not been shown to outperform competitive unsupervised methods based on a single feature such as the presence of ...
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Interpreting ROUGE scores

I recently read the paper on Salesforce's advances in abstractive text summarisation. This states that the ROUGE-1 score achieved of 41.16 is significantly better than the previous state of the art. ...
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How to determine summary like tables on any informative web (html) page

I am struggling with determining the best way to guess which table (if any) on a given web page is the summary table. Examples would be the first, right-side tables on these pages. http://wikitravel....
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1answer
307 views

Document summarization with Log-likelihood ratio

I am trying to implement a text summary using Log-Likelihood Ratio. As explained in https://www.cs.bgu.ac.il/~elhadad/nlp16/nenkova-mckeown.pdf under section 2.1 I do not understand what do they ...
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Recall and precision in text summarization

As you know extractive text summarization is a binary classification problem!(a sentence should be included in summary or not). we have developed our text summarization system with three different ...
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1answer
516 views

Gaming the ROUGE metric for text summarization

ROUGE seems to be the standard way of evaluating the quality of machine generated summaries of text documents by comparing them with reference summaries (human generated). $$ROUGE_{n}= \frac {\sum_{s\...
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Summarization of text documents (legal domain) using deep learning techniques

I am referring to the site deeplearning.net on how to implement the deep learning architectures. I have read quite a few research papers on document summarization (both single document and ...
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Statistical Analysis on Comments and Feedback

I have access to 10,000 comments for a mobile app, and I want to run some interesting statistical analysis on them. What I have done so far: Look at the frequency of each word in all the comments. ...
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Log-likelihood ratio in document summarization

I initially asked this on stack overflow and was referred to this site, so here goes: I am implementing some unsupervised methods of content-selection/extraction based document summarization and I'm ...