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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44 views

Machine learning for product names

I have a machine learning challenge I may be over thinking. I have a set of 3.5 million products (not unique, there are multiple instances of each product). Each product has a "description" from it's ...
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21 views

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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105 views

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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22 views

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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1answer
31 views

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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1answer
41 views

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
29 views

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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12 views

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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1answer
51 views

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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25 views

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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2answers
5k views

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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44 views

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
359 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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1answer
476 views

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
550 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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1answer
1k views

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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50 views

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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1answer
2k views

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 ...