Answers.Body: <p>If the structure of both <span class="math-container">$T_1$</span> and <span class="math-container">$T_2$</span> is bullet points, I presume that the similarity between every pair of bullet points is a nice baseline. I'd go through various encoders and see how they compare.
  Otherwise, if <span class="math-container">$T_2$</span> is a paragraph, maybe split it to sentences and treat those as bullet points.
  Other option (which might be harder) is to fine-tune a language model with the following input and label:
  suppose the bullet points are <span class="math-container">$\{bp_1^1,...,bp_1^n\}$</span> for <span class="math-container">$T_1$</span> and <span class="math-container">$\{bp_2^1,...,bp_2^m\}$</span> for <span class="math-container">$T_2$</span>, we can formulate a prompt as</p>
  <blockquote>
  <p>Input: <code>text1</code>=<span class="math-container">$bp_1^i$</span>, <code>text2</code>=<span class="math-container">$bp_2^j$</span></p>
  <p>Output: <code>True</code> <em>iff</em> <code>text2</code> in <code>text1</code></p>
  </blockquote>
  <p>Where the targets were tagged by you apriori</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-11-06T18:23:22.387Z
Answers.Text: How to split into sentences when the same point could be spread across two or more sentences in T2? Also how to order the points in T2 after the split?
Answers.UserDisplayName: learner
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Answers.CreationDate: 2022-11-06T23:45:31.670Z
Answers.Text: And what do you mean by "language model"?
Answers.UserDisplayName: learner
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-11-07T00:53:18.477Z
Answers.Text: Lastly, this won't be contributing anything new to preexisting research right?
Answers.UserDisplayName: learner
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Answers.CreationDate: 2022-11-06T18:12:33.010Z
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Answers.UserDisplayName: Hadar Sharvit
Body: <p>I am working on a problem where I have two texts T1, T2. T1 contains some important points that I have entered. How can I make sure that T2 has those points? I am aware of the algorithms like cosine, jaccard, BERT for semantic similarity but the problem is that they apply to the whole text whereas I want point-wise similarity i.e. T2 must contain the T1 points although the order and words used may differ a bit.</p>
  <p>By points I meant bullet points covering discrete concepts and I basically want to check how many discrete concepts in my T1's points are covered in T2 where they could be in a single sentence or spread out across multiple sentences.</p>
  <p>Example:</p>
  <p>So T1 could have the following two points:</p>
  <ul>
  <li>The Queen reigned from 1943 to 2022.</li>
  <li>The Queen was the second
  longest reigning monarch.</li>
  </ul>
  <p>Now T2 could either be:</p>
  <p>The Queen was the second longest reigning monarch with her reign spanning 1952 to 2022.</p>
  <p>or</p>
  <ul>
  <li>The Queen reigned from 1952 to 2022.</li>
  <li>She was Britain's second longest monarch.</li>
  </ul>
  <p>In both these cases, T2 should be considered to contain both points in T1.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-11-06T14:29:15.507Z
Comments.Text: what do you mean by points
Comments.UserDisplayName: Hadar Sharvit
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-11-06T16:55:41.790Z
Comments.Text: @HadarSharvit edited to add an example.
Comments.UserDisplayName: learner
Tags: <natural-language-processing>
Title: Matching sentences (/bullet points) in two sets using NLP
UserDisplayName: learner
ThreadText: # Matching sentences (/bullet points) in two sets using NLP
  
  (Asked by: username_0 on 2022-11-06)
  
  I am working on a problem where I have two texts T1, T2. T1 contains some important points that I have entered. How can I make sure that T2 has those points? I am aware of the algorithms like cosine, jaccard, BERT for semantic similarity but the problem is that they apply to the whole text whereas I want point-wise similarity i.e. T2 must contain the T1 points although the order and words used may differ a bit.
  
  By points I meant bullet points covering discrete concepts and I basically want to check how many discrete concepts in my T1's points are covered in T2 where they could be in a single sentence or spread out across multiple sentences.
  
  Example:
  
  So T1 could have the following two points:
  
  * The Queen reigned from 1943 to 2022.
  * The Queen was the second
    longest reigning monarch.
  
  Now T2 could either be:
  
  The Queen was the second longest reigning monarch with her reign spanning 1952 to 2022.
  
  or
  
  * The Queen reigned from 1952 to 2022.
  * She was Britain's second longest monarch.
  
  In both these cases, T2 should be considered to contain both points in T1.
  
  **Tags:** natural-language-processing
  
  > **Comments:**
  > **username_1**: what do you mean by points  
  > **username_0**: @username_1 edited to add an example.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2022-11-06. Score: 1
  
  If the structure of both $T\_1$ and $T\_2$ is bullet points, I presume that the similarity between every pair of bullet points is a nice baseline. I'd go through various encoders and see how they compare.
  Otherwise, if $T\_2$ is a paragraph, maybe split it to sentences and treat those as bullet points.
  Other option (which might be harder) is to fine-tune a language model with the following input and label:
  suppose the bullet points are $\{bp\_1^1,...,bp\_1^n\}$ for $T\_1$ and $\{bp\_2^1,...,bp\_2^m\}$ for $T\_2$, we can formulate a prompt as
  
  > Input: `text1`=$bp\_1^i$, `text2`=$bp\_2^j$
  >
  > Output: `True` *iff* `text2` in `text1`
  
  Where the targets were tagged by you apriori
  
  > **Comments:**
  > **username_0**: How to split into sentences when the same point could be spread across two or more sentences in T2? Also how to order the points in T2 after the split?  
  > **username_0**: And what do you mean by "language model"?  
  > **username_0**: Lastly, this won't be contributing anything new to preexisting research right?  
  
  ---
  
  