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Text Summarization of GitHub Issues

Note: This core program is originally from this medium article's tutorial to get started on GRU RNNs in NLP.

What is it?

Using a dataset of GitHub Issues' titles, bodies and URLs, a Sequence to Sequence model is constructed with GRUs to summarize the GitHub issue body. The machine generated title is better and more compact than the actual user defined title.

Using approximate nearest neighbors search it also finds out the most closely related GitHub Issues by Euclidean distance. The Spotify ANNOY package is used for this purpose.

The model's BLEU score is also obtained.

Note: Training the dataset for this model is computationally expensive owing to the large size of the dataset being over 8M entries.

Architecture

Overall

Layers

Text Summarisation of Github Issues with NLP

GitHub Issues are known to be excessively long and complicated. It would be a great help to the community, if the issues could be summarized into a precise single line description using Natural Language Processing.

Dataset

The dataset can be found here

About

Natural Language Processing (CSE4022) Text Summarisation Project

Topics

github-issuesnlptext-summarisation

Resources

Readme
Activity

Stars

0 stars

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0 watching

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2 forks
Report repository

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Text Summarization of GitHub Issues
Note: This core program is originally from this medium article's tutorial to get started on GRU RNNs in NLP.
What is it?
Using a dataset of GitHub Issues' titles, bodies and URLs, a Sequence to Sequence model is constructed with GRUs to summarize the GitHub issue body. The machine generated title is better and more compact than the actual user defined title.
Using approximate nearest neighbors search it also finds out the most closely related GitHub Issues by Euclidean distance. The Spotify ANNOY package is used for this purpose.
The model's BLEU score is also obtained.
Note: Training the dataset for this model is computationally expensive owing to the large size of the dataset being over 8M entries.
Architecture
Overall
Layers
Text Summarisation of Github Issues with NLP
GitHub Issues are known to be excessively long and complicated. It would be a great help to the community, if the issues could be summarized into a precise single line description using Natural Language Processing.
Dataset
The dataset can be found here
About
Natural Language Processing (CSE4022) Text Summarisation Project
Topics
github-issuesnlptext-summarisation
Resources
Readme
Activity
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
Text Summarization of GitHub Issues
Note: This core program is originally from this medium article's tutorial to get started on GRU RNNs in NLP.
What is it?
Using a dataset of GitHub Issues' titles, bodies and URLs, a Sequence to Sequence model is constructed with GRUs to summarize the GitHub issue body. The machine generated title is better and more compact than the actual user defined title.
Using approximate nearest neighbors search it also finds out the most closely related GitHub Issues by Euclidean distance. The Spotify ANNOY package is used for this purpose.
The model's BLEU score is also obtained.
Note: Training the dataset for this model is computationally expensive owing to the large size of the dataset being over 8M entries.
Architecture
Overall
Layers
Text Summarisation of Github Issues with NLP
GitHub Issues are known to be excessively long and complicated. It would be a great help to the community, if the issues could be summarized into a precise single line description using Natural Language Processing.
Dataset
The dataset can be found here
About
Natural Language Processing (CSE4022) Text Summarisation Project
Topics
github-issuesnlptext-summarisation
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
2 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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textSummarisation.ipynb
Repository files navigation
Text Summarization of GitHub Issues
Note: This core program is originally from this medium article's tutorial to get started on GRU RNNs in NLP.
What is it?
Using a dataset of GitHub Issues' titles, bodies and URLs, a Sequence to Sequence model is constructed with GRUs to summarize the GitHub issue body. The machine generated title is better and more compact than the actual user defined title.
Using approximate nearest neighbors search it also finds out the most closely related GitHub Issues by Euclidean distance. The Spotify ANNOY package is used for this purpose.
The model's BLEU score is also obtained.
Note: Training the dataset for this model is computationally expensive owing to the large size of the dataset being over 8M entries.
Architecture
Overall
Layers
Text Summarisation of Github Issues with NLP
GitHub Issues are known to be excessively long and complicated. It would be a great help to the community, if the issues could be summarized into a precise single line description using Natural Language Processing.
Dataset
The dataset can be found here
About
Natural Language Processing (CSE4022) Text Summarisation Project
Topics
github-issuesnlptext-summarisation
Resources
Readme
Activity
Stars
0 stars
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0 watching
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2 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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textSummarisation.ipynb
Repository files navigation
Text Summarization of GitHub Issues
Note: This core program is originally from this medium article's tutorial to get started on GRU RNNs in NLP.
What is it?
Using a dataset of GitHub Issues' titles, bodies and URLs, a Sequence to Sequence model is constructed with GRUs to summarize the GitHub issue body. The machine generated title is better and more compact than the actual user defined title.
Using approximate nearest neighbors search it also finds out the most closely related GitHub Issues by Euclidean distance. The Spotify ANNOY package is used for this purpose.
The model's BLEU score is also obtained.
Note: Training the dataset for this model is computationally expensive owing to the large size of the dataset being over 8M entries.
Architecture
Overall
Layers
Text Summarisation of Github Issues with NLP
GitHub Issues are known to be excessively long and complicated. It would be a great help to the community, if the issues could be summarized into a precise single line description using Natural Language Processing.
Dataset
The dataset can be found here
About
Natural Language Processing (CSE4022) Text Summarisation Project
Topics
github-issuesnlptext-summarisation
Resources
Readme
Activity
Stars
0 stars
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0 watching
Forks
2 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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textSummarisation.ipynb
Repository files navigation
Text Summarization of GitHub Issues
Note: This core program is originally from this medium article's tutorial to get started on GRU RNNs in NLP.
What is it?
Using a dataset of GitHub Issues' titles, bodies and URLs, a Sequence to Sequence model is constructed with GRUs to summarize the GitHub issue body. The machine generated title is better and more compact than the actual user defined title.
Using approximate nearest neighbors search it also finds out the most closely related GitHub Issues by Euclidean distance. The Spotify ANNOY package is used for this purpose.
The model's BLEU score is also obtained.
Note: Training the dataset for this model is computationally expensive owing to the large size of the dataset being over 8M entries.
Architecture
Overall
Layers
Text Summarisation of Github Issues with NLP
GitHub Issues are known to be excessively long and complicated. It would be a great help to the community, if the issues could be summarized into a precise single line description using Natural Language Processing.
Dataset
The dataset can be found here
About
Natural Language Processing (CSE4022) Text Summarisation Project
Topics
github-issuesnlptext-summarisation
Resources
Readme
Activity
Stars
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
Text Summarization of GitHub Issues
Note: This core program is originally from this medium article's tutorial to get started on GRU RNNs in NLP.
What is it?
Using a dataset of GitHub Issues' titles, bodies and URLs, a Sequence to Sequence model is constructed with GRUs to summarize the GitHub issue body. The machine generated title is better and more compact than the actual user defined title.
Using approximate nearest neighbors search it also finds out the most closely related GitHub Issues by Euclidean distance. The Spotify ANNOY package is used for this purpose.
The model's BLEU score is also obtained.
Note: Training the dataset for this model is computationally expensive owing to the large size of the dataset being over 8M entries.
Architecture
Overall
Layers
Text Summarisation of Github Issues with NLP
GitHub Issues are known to be excessively long and complicated. It would be a great help to the community, if the issues could be summarized into a precise single line description using Natural Language Processing.
Dataset
The dataset can be found here
About
Natural Language Processing (CSE4022) Text Summarisation Project
Topics
github-issuesnlptext-summarisation
Resources
Readme
Activity
Stars
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0 watching
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2 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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Repository files navigation
Text Summarization of GitHub Issues
Note: This core program is originally from this medium article's tutorial to get started on GRU RNNs in NLP.
What is it?
Using a dataset of GitHub Issues' titles, bodies and URLs, a Sequence to Sequence model is constructed with GRUs to summarize the GitHub issue body. The machine generated title is better and more compact than the actual user defined title.
Using approximate nearest neighbors search it also finds out the most closely related GitHub Issues by Euclidean distance. The Spotify ANNOY package is used for this purpose.
The model's BLEU score is also obtained.
Note: Training the dataset for this model is computationally expensive owing to the large size of the dataset being over 8M entries.
Architecture
Overall
Layers
Text Summarisation of Github Issues with NLP
GitHub Issues are known to be excessively long and complicated. It would be a great help to the community, if the issues could be summarized into a precise single line description using Natural Language Processing.
Dataset
The dataset can be found here
About
Natural Language Processing (CSE4022) Text Summarisation Project
Topics
github-issuesnlptext-summarisation
Resources
Readme
Activity
Stars
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Report repository
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