Using Disvow

Using Disvow
Question about using disvow. For local seo, I have citations that I made in the past, and the address was different back then. So I changed most citations, however a few I can't change at all, I tried contacting the directories but hear nothing from them. What I plan to do is disvow those links. Now my question is, does google disvow the entire domain, or just the subdomain? Because I want to create new accounts on the site with the new address updated.
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I would avoid disavow if you can . Generally it does not have an affect
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A disavow doesn't fix any data inconsistancy and there is no point in disavowing these citations. The link isn't the problem, it's the outdated data. In general, disavowing is possible on exact URL level or on (sub)domain level.
From my experience: After an adress change, there will always be some old and outdated profiles/citations/local directory entries left. As long as the majority and the important ones like e.g. Google My Business, Yelp and similar are changed, that's never a problem.
So what's the solution? Cause it's a strong local citation and they don't answer to emails...

How to Use BERT to Generate Meta Descriptions at Scale

The text output is in one line, we will split it with the next code.

text = text.splitlines(True) #keep newlines
I removed the first line that includes the code for the sponsored ad and the last few lines that include some article meta data.

text = text[1:-5] #remove sponsor code and end meta data
Finally, I can write down the article content to a text file using this code.

with open("python-data-stories.txt", "a") as f:

  f.writelines(text)
After this, we are ready to move to the summarization step.

Generating the Text Summary

We will generate an abstractive summary, but before we can generate it, we need to modify the file summarize.py.

In order to keep things simple, I created a patch file with the changes that you can download with the following code.

!wget https://gist.githubusercontent.com/hamletbatista/f2741a3a74e4c5cc46ce9547b489ec36/raw/ccab9cc3376901b2f6b0ba0c4bbd03fa48c1b159/summarizer.patch
You can review the changes it is going to make here. Red lines will be removed and green ones will be added.

I borrowed these changes from the notebook linked above, and they enable us to pass files to summarize and see the results.

You can apply the changes using this.

!patch < summarizer.patch
We have one final preparatory step. The next code downloads some tokenizers needed by the summarizer.

import nltk
nltk.download('punkt')
Finally, let’s generate our summary with the following code.

#CNN_DM abstractive

%cd /content/PreSumm/src

!python summarizer.py -task abs -mode test -test_from /content/PreSumm/models/CNN_DailyMail_Abstractive/model_step_148000.pt -batch_size 32 -test_batch_size

500 -bert_data_path ../bert_data/cnndm -log_file ../logs/val_abs_bert_cnndm -report_rouge False  -sep_optim true -use_interval true -visible_gpus -1 -

max_pos 512 -max_src_nsents 100 -max_length 200 -alpha 0.95 -min_length 50 -result_path ../results/abs_bert_cnndm_sample
Here is what the partial output looks like.
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