device date act power 1 react power 2 ------------------------------------------------- M1 05-02 2 3 M2 05-02 4 2 M3 05-02 3 4 M1 06-02 1 2 M2 07-02 3 4 ------- ------- need sum need sum Say that I only need the sum of M1 and M2 from that table. How could I add a variable that contains the sum of power group by date and device? I don't know if it is desired to have something like this? Or how …

Does anybody know or can help me to find the source paper of the method "Reduction" of the library "sumy"? This method is here: Reduction where it says that it is based on here, but in none of these places I am able to find the source. At this description, it only says that: "Graph-based summarization, where a sentence salience is computed as the sum of the weights of its edges to other sentences. The weight of an edge between …

I am very new to nlp. I am doing a text segmentation task and for evaluating my model I need to calculate Pk and Windiff scores. My question is what is the ideal value for window size (k) for Pk score because different window sizes give different results. I am using this function nltk.metrics.segmentation.pk. Thanks.

I am trying to fine tune t5-small for text summarization, and I have the following graph loss per batch: and learning rate per batch: Do you think loss graph is normal (regarding this use case) or there is a problem

Traditionally, for evaluation, the reference in ROUGE is human generated text (summary) which we compare with system generated text (summary). So consider this, if we generate summaries with different algorithms, TextRank, LexRank, Luhn, and Gensim. Then we take the generated summary as hypothesis and original text document as a reference in ROUGE and calculate the R, P, F1 for each summary. Would the scores tell us which model captures more information form orgional text? For example, for 250 words summary …

We are using different pre-trained models in python transformers library, to generate summaries(both extractive and abstractive). So is there a programmatic way, based on the output summary, we can classify it as abstractive or extractive? One method I think of is using the rouge python library to compute rouge score with respect to original input text(not human reference summary), which will have rouge(specifically LCS) precision score as 1.0 for extractive(since all the words present in summary will be present in …

I have a corpus of documents whose some lines have information like this: wt 210 1b 14.4 oz (98 kg) or weight: 219 lb (99 kg), height: 5' 1.9" (157 cm) The format of occurrence of such information varies from document to document. I need the value or the substring corresponding to weight and weight only. Here are my questions regarding the problem: I have certain regexes that can get the weight value for labeling the lines. However, I do …

Can BART, PEGASUS ... etc. API in huggingface library be used to directly perform multi document summarization? (e.g. here: https://huggingface.co/transformers/model_doc/bart.html)

The UniLM claims to be the best approach for summarization task. But there doesn't seem to be any tutorial or how-to section in the README.md or any other blog. How exactly can I use this state-of-the-art library for abstractive summary generation? Github link Paper P.S. A newbie in NLP. Sorry if this is a dumb question.

I got Column1, Column2, Column3, Column4, Column5, Column6 I'd like to group Column1 and get the row sum of Column3,4 and 5 When I apply groupby() and get this that is correct but it's leaving out Column6: df = df.groupby(['Column1'])[['Column3', 'Column4', 'Column5']].sum I tried with this but it doesn't group according to Column1 and it doesn't sum anything, but I get all my columns: df.sort_values(['Column1']).groupby(['Column3', 'Column4', 'Column5']).sum() How can I use groupby() correctly in this case? Thank you! I add …

I am working on Single Document Summarization task on News datasets. I do some experiments in this task. A simple experiment that I make and has a good result is extracting sentences just from beginning of news. Now I want to find any paper or research result about this type of sentence selection. Is there any research to show how good is to choose sentences just from beginning of text without any reordering?

I'm working on an automatic text summarization NLP problem and looking for a dataset with USA legal case reports similar to the Australian legal case reports dataset in UCI repository. Can you please refer me to any such dataset? I've not been able to find one up until now. It will also be great if you can point me to other industry-relevant datasets that can be used for automatic text summarization.

Introduction I am looking to extract out sentence(s) from a news article for questions like 'who', 'when', 'what', 'why' and 'how'. Now I did some research and found bert model which can be utilised to make query based summarizer. But it was not satisfactory as it sentences extracted were small and wrong by huge margins. It makes sense as it was designed to answer full questions and not something like just 'when'. Spacy I knew about spacy and from that …

I am trying to make a text summarization program that will take a text article and reduce it to a para or 2. Since I am a newbie with no idea of NLP, it is hard to approach and break down the problem. So I was wondering if there was a measure that is used to check for effectiveness and correctness of text summarization. I tried googling this, but nothing that suits my purpose. Does something like this even exist? …

Data: Each corresponds to an event (a person's visit to the hospital, as an example). I have a series of data associated with this event (duration of visit, motive, etc...). Objective: Summarize the above information in a per person data set (meaning that the new data set should have only on row per person and capturing as much information about their history as possible). My initial solutions: 1 - The most obvious, and potentially useful, is to create relevant variables …

I am reviewing summarization techniques and haven't (yet) found an approach to limit the length of a summary. So for example a summarization function that gives me a summary that is < 500 words. Can you point me in the right direction? Are there approaches/implementations out there that try to solve this challenge? Appreciate your replies!

I need to detect list of change points in time series dataset (temperature), and I need to split dataset into set of classes (patterns) and detect seasonality of each class (pattern). for example suppose that we create 2-classes named called and hot, each class contains readings that represent each state, I need to know seasonality of these state over the whole time period so that I can summarize dataset as hot state occurs from the time X to Y over …

I have a large collection of project manuals, each with a large number of pages. Each manual contains some form of summary paragraphs, although these are not necessarily similar in structure or format from one to the next. The rest of the manual generally contains a large amount of various information in relation to the project, and is not always relevant to the desired content to be extracted and summarized. In theory- paragraph 1 - Project Summary (Extract this) paragraph …

I am working on an automated insights generation use case where I want to generate meaningful sentences from given aggregated data. For example, Data: Student = John Total_Marks = 96 Class_Average = 85 NLG model-generated insights: 1. You did an excellent job, John! Your score is 96! 2. You have scored 11 marks above the class average. When I look at classic NLG, they are sentence generation approaches given a starting letter or word. This might be more of a …

My table looks like this: Tissue Dry Amount Analyte Area 1 Liver A a 3-Phosphoglyceric Acid 66351918.4 2 Liver B a 3-Phosphoglyceric Acid 119013081.6 3 Liver A b 3-Phosphoglyceric Acid 195732464.0 4 Liver B b 3-Phosphoglyceric Acid 247443210.8 5 Liver A c 3-Phosphoglyceric Acid 456447252.7 6 Liver B c 3-Phosphoglyceric Acid 494555301.1 I would like to get the mean of the two values for the same Tissue Amount and Analyte by ignoring the DRY variable. I always end up having …