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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Google android-app's titles and soft clustering
I have a no-trial question: I want to soft cluster the apps from Google Store. Most of the parameters are numbers, so no big clue. There are also "tags" but this is like using categorical ...
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Good architecture/approach for encoding text
I want to create a model that efficiently encodes text for retrieving images that match the description given in the text.
I have extracted features of images through VGG19 model(4096 features for ...
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What is the effect of changing the weight decay and warm-up steps in fine-tuning PEGASUS?
I am fine-tuning PEGASUS model using this script.
I am currently using the SAMSum dataset and I have reached a point in which the output doesn't get better.
Examples:
The Actual Summary
Alexis and ...
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ROUGE scores for extractive vs abstractive text summarization
The ROUGE score (scores) allows us to measure (although not in a perfect way) the quality of our text summarization by computing the frequency of overlapping n-grams between our produced summary and ...
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How to test generated text
I am creating a text generation algorithm for my master's research. I have a dialogue between two people and I would like to simulate one part of the conversation with naturally generated text (not ...
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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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What are most recent research work on the problem of key phrases extraction from a text corpus?
I am interested in the problem of extracting key phrases from a text corpus. This is different from the keyword extraction problem, which is only for a particular document. This problem helps us, for ...
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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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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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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 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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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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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 ...
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