Stack Exchange Network

Stack Exchange network consists of 174 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers.

Visit Stack Exchange

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.

1
vote
0answers
15 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 ...
0
votes
1answer
30 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 ...
0
votes
1answer
33 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 ...
0
votes
1answer
26 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),...
0
votes
0answers
13 views

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 ...
1
vote
0answers
11 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 ...
0
votes
0answers
23 views

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: ...
0
votes
0answers
64 views

Is perplexity measure [PP] enough to evaluate n-gram language models?

I just wanted to know if the application of the perplexity measure is enough for evaluating the power of an n-gram LM?
2
votes
1answer
41 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 ...
1
vote
0answers
43 views

How to extract text highlight like in Google Play Review highlights? [closed]

I am trying to extract text highlights like you can see in Google Play. Tried to extract n-grams but there is too much crap. What approach should be used to get relevant useful highlights?
3
votes
0answers
23 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 ...
3
votes
2answers
2k 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. ...
2
votes
0answers
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....
2
votes
1answer
278 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 ...
2
votes
1answer
354 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 ...
1
vote
1answer
504 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\...
5
votes
1answer
953 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 ...
1
vote
0answers
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. ...
8
votes
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 ...