Answers.Body: <p>I have out-of-date experience of working in this field, but I thought it might be useful to give an answer based on what I was doing approximately 10 years ago just to give background to any other answers that might describe more modern techniques.</p>
  <p>At the time, giving a long form text as input to a neural network (which I suspect will be the modern approach) would have required a lot more processing power/memory than my employer was willing to dedicate to the task, so we worked on a different approach.  We used a variety of well-known analytical algorithms (all of which were at the time implemented by the Stanford &quot;CoreNLP&quot; library) to tag the text with a variety of useful annotations, identifying proper nouns, references between the same object in different parts of the text, and so on.  We then applied scores to each sentence and to the words within the sentence based on bonuses and penalties calculated from (eg) whether the sentence introduces a named object that is referred to again, or based on its position in the document or paragraph, and so on.</p>
  <p>We then found the most highly ranking sentences and produced the summary based on those, deleting low-ranking words within the sentences (typically adjectives and adverbs that were not mentioned again).  Some of the scores had to be recalculated each time a new sentence was added to the output (e.g. there was a bonus for the sentences in the same paragraph as a sentence that was included).</p>
  <p>The values of the scores and penalties were optimized using a genetic algorithm in order to attempt to replicate a training set of hand-produced summaries.</p>
  <p>The results were not brilliant, but the algorithm did produce meaningful summaries in most cases, although I believe my employer (I was hired as a freelancer to implement this system which was designed by the employer) never launched his planned commercial service based on the system.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-02-18T20:35:11.183Z
Answers.LastActivityDate: 2023-02-18T20:46:25.710Z
Answers.LastEditDate: 2023-02-18T20:46:25.710Z
Answers.UserDisplayName: occipita
Body: <p>With users expecting instantaneous information and no compromise on in-depth details, app developers are challenged to condense long-form content such as articles, editorials, and discussion threads into concise summaries. To ensure that users still gain valuable insights and information, it is important to determine the most effective methods and tools to summarize such content.</p>
  <p>Are there any viable algorithms or libraries that are proven to produce summaries without sacrificing the important details? Any insights or suggestions on the best practices to address this problem would be much appreciated.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-02-18T20:20:38.340Z
Comments.Text: Not convinced "gpt" is a useful tag to this question.  I don't think the GPT architecture would be a good match to this problem, which asks for inputs to be "long form content", while GPT has a (relatively) small context size that makes it inappropriate for working on such texts.  A useful answer to this question is likely to involve some kind of recurrent network, in order to allow for arbitrary length inputs, I suspect.
Comments.UserDisplayName: occipita
Tags: <machine-learning><natural-language-processing><algorithm-request><text-summarization><natural-language-generation>
Title: What are the most effective methods and tools for summarizing long-form content like articles, editorials, and discussion threads for an app?
UserDisplayName: Anshu Kumar Gupta
ThreadText: # What are the most effective methods and tools for summarizing long-form content like articles, editorials, and discussion threads for an app?
  
  (Asked by: username_0 on 2023-02-18)
  
  With users expecting instantaneous information and no compromise on in-depth details, app developers are challenged to condense long-form content such as articles, editorials, and discussion threads into concise summaries. To ensure that users still gain valuable insights and information, it is important to determine the most effective methods and tools to summarize such content.
  
  Are there any viable algorithms or libraries that are proven to produce summaries without sacrificing the important details? Any insights or suggestions on the best practices to address this problem would be much appreciated.
  
  **Tags:** machine-learning, natural-language-processing, algorithm-request, text-summarization, natural-language-generation
  
  > **Comments:**
  > **username_1**: Not convinced "gpt" is a useful tag to this question.  I don't think the GPT architecture would be a good match to this problem, which asks for inputs to be "long form content", while GPT has a (relatively) small context size that makes it inappropriate for working on such texts.  A useful answer to this question is likely to involve some kind of recurrent network, in order to allow for arbitrary length inputs, I suspect.  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2023-02-18. Score: 2
  
  I have out-of-date experience of working in this field, but I thought it might be useful to give an answer based on what I was doing approximately 10 years ago just to give background to any other answers that might describe more modern techniques.
  
  At the time, giving a long form text as input to a neural network (which I suspect will be the modern approach) would have required a lot more processing power/memory than my employer was willing to dedicate to the task, so we worked on a different approach. We used a variety of well-known analytical algorithms (all of which were at the time implemented by the Stanford "CoreNLP" library) to tag the text with a variety of useful annotations, identifying proper nouns, references between the same object in different parts of the text, and so on. We then applied scores to each sentence and to the words within the sentence based on bonuses and penalties calculated from (eg) whether the sentence introduces a named object that is referred to again, or based on its position in the document or paragraph, and so on.
  
  We then found the most highly ranking sentences and produced the summary based on those, deleting low-ranking words within the sentences (typically adjectives and adverbs that were not mentioned again). Some of the scores had to be recalculated each time a new sentence was added to the output (e.g. there was a bonus for the sentences in the same paragraph as a sentence that was included).
  
  The values of the scores and penalties were optimized using a genetic algorithm in order to attempt to replicate a training set of hand-produced summaries.
  
  The results were not brilliant, but the algorithm did produce meaningful summaries in most cases, although I believe my employer (I was hired as a freelancer to implement this system which was designed by the employer) never launched his planned commercial service based on the system.
  
  
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