| "sourcesContent": ["export const TREEBANK_CONTRACTIONS: RegExp[] = [\n /\\b(can)(not)\\b/i,\n /\\b(d)('ye)\\b/i,\n /\\b(gim)(me)\\b/i,\n /\\b(gon)(na)\\b/i,\n /\\b(got)(ta)\\b/i,\n /\\b(lem)(me)\\b/i,\n /\\b(more)('n)\\b/i,\n /\\b(wan)(na) /i,\n / ('t)(is)\\b/i,\n / ('t)(was)\\b/i,\n];\n\nexport const HONORIFICS: string[] = [\n 'jr',\n 'mr',\n 'mrs',\n 'ms',\n 'dr',\n 'prof',\n 'sr',\n 'sen',\n 'corp',\n 'rep',\n 'gov',\n 'atty',\n 'supt',\n 'det',\n 'rev',\n 'col',\n 'gen',\n 'lt',\n 'cmdr',\n 'adm',\n 'capt',\n 'sgt',\n 'cpl',\n 'maj',\n 'miss',\n 'misses',\n 'mister',\n 'sir',\n 'esq',\n 'mstr',\n 'phd',\n 'adj',\n 'adv',\n 'asst',\n 'bldg',\n 'brig',\n 'comdr',\n 'hon',\n 'messrs',\n 'mlle',\n 'mme',\n 'op',\n 'ord',\n 'pvt',\n 'reps',\n 'res',\n 'sens',\n 'sfc',\n 'surg',\n];\n\nexport const ABBR_COMMON: string[] = [\n 'arc',\n 'al',\n 'exp',\n 'rd',\n 'st',\n 'dist',\n 'mt',\n 'fy',\n 'pd',\n 'pl',\n 'plz',\n 'tce',\n 'llb',\n 'md',\n 'bl',\n 'ma',\n 'ba',\n 'lit',\n 'ex',\n 'e.g',\n 'i.e',\n 'circa',\n 'ca',\n 'cca',\n 'v.s',\n 'etc',\n 'esp',\n 'ft',\n 'b.c',\n 'a.d',\n];\n\nexport const ABBR_ORGANIZATIONS: string[] = [\n 'co',\n 'corp',\n 'yahoo',\n 'joomla',\n 'jeopardy',\n 'dept',\n 'univ',\n 'assn',\n 'bros',\n 'inc',\n 'ltd',\n];\n\nexport const ABBR_PLACES: string[] = [\n 'ala',\n 'ariz',\n 'ark',\n 'cal',\n 'calif',\n 'col',\n 'colo',\n 'conn',\n 'del',\n 'fed',\n 'fla',\n 'fl',\n 'ga',\n 'ida',\n 'ind',\n 'ia',\n 'la',\n 'kan',\n 'kans',\n 'ken',\n 'ky',\n 'la',\n 'md',\n 'mich',\n 'minn',\n 'mont',\n 'neb',\n 'nebr',\n 'nev',\n 'okla',\n 'penna',\n 'penn',\n 'pa',\n 'dak',\n 'tenn',\n 'tex',\n 'ut',\n 'vt',\n 'va',\n 'wash',\n 'wis',\n 'wisc',\n 'wy',\n 'wyo',\n 'usafa',\n 'alta',\n 'ont',\n 'que',\n 'sask',\n 'yuk',\n 'ave',\n 'blvd',\n 'cl',\n 'ct',\n 'cres',\n 'hwy',\n 'U.S',\n 'U.S.A',\n 'E.U',\n 'N\u00B0',\n];\n\nexport const ABBR_TIME: string[] = ['a.m', 'p.m'];\n\nexport const ABBR_DATES: string[] = [\n 'jan',\n 'feb',\n 'mar',\n 'apr',\n 'jun',\n 'jul',\n 'aug',\n 'sep',\n 'sept',\n 'oct',\n 'nov',\n 'dec',\n];\n\nexport const GATE_EXCEPTIONS: string[] = [\n 'ex',\n 'e.g',\n 'i.e',\n 'circa',\n 'ca',\n 'cca',\n 'v.s',\n 'esp',\n 'ft',\n 'st',\n 'mt',\n ...HONORIFICS,\n];\n\nexport const GATE_SUBSTITUTIONS: string[] = [\n ...ABBR_COMMON,\n ...ABBR_DATES,\n ...ABBR_ORGANIZATIONS,\n ...ABBR_PLACES,\n ...ABBR_TIME,\n ...HONORIFICS,\n];\n", "import { GATE_EXCEPTIONS, GATE_SUBSTITUTIONS, TREEBANK_CONTRACTIONS } from './constants';\n\n/**\n * Splits a sentence into an array of word tokens\n * in accordance with the Penn Treebank guidelines.\n *\n * NOTE: This method assumes that the input is a single\n * sentence only. Providing multiple sentences within a\n * single string can trigger edge cases which have not\n * been accounted for.\n *\n * Adapted from Titus Wormer's port of the Penn Treebank Tokenizer\n * found at https://gist.github.com/wooorm/8504606\n *\n *\n * @method treeBankTokenize\n * @param {string} input The sentence to be tokenized\n * @return {Array<string>} An array of word tokens\n */\nexport function treeBankTokenize(input: string): string[] {\n if (input.length === 0) {\n return [];\n }\n\n // Does the following things in order of appearance by line:\n // 1. Replace quotes at the sentence start position with double ticks\n // 2. Wrap spaces around a double quote preceded by opening brackets\n // 3. Wrap spaces around a non-unicode ellipsis\n // 4. Wrap spaces around some punctuation signs (,;@#$%&)\n // 5. Wrap spaces around a period and zero or more closing brackets\n // (or quotes), when not preceded by a period and when followed\n // by the end of the string. Only splits final periods because\n // sentence tokenization is assumed as a preprocessing step\n // 6. Wrap spaces around all exclamation marks and question marks\n // 7. Wrap spaces around opening and closing brackets\n // 8. Wrap spaces around en and em-dashes\n let parse = input\n .replace(/^\"/, ' `` ')\n .replace(/([ ([{<])\"/g, '$1 `` ')\n .replace(/\\.\\.\\.*/g, ' ... ')\n .replace(/[;@#$%&]/g, ' $& ')\n .replace(/([^.])(\\.)([\\])}>\"']*)\\s*$/g, '$1 $2$3 ')\n .replace(/[,?!]/g, ' $& ')\n .replace(/[\\][(){}<>]/g, ' $& ')\n .replace(/---*/g, ' -- ');\n\n // Wrap spaces at the start and end of the sentence for consistency\n // i.e. reduce the number of Regex matches required\n parse = ` ${parse} `;\n\n // Does the following things in order of appearance by line:\n // 1. Replace double quotes with a pair of single quotes wrapped with spaces\n // 2. Wrap possessive or closing single quotes\n // 3. Add a space before single quotes followed by `s`, `m`, or `d` and a space\n // 4. Add a space before occurrences of `'ll`, `'re`, `'ve` or `n't`\n parse = parse\n .replace(/\"/g, \" '' \")\n .replace(/([^'])' /g, \"$1 ' \")\n .replace(/'([sSmMdD]) /g, \" '$1 \")\n .replace(/('ll|'LL|'re|'RE|'ve|'VE|n't|N'T) /g, ' $1 ');\n\n for (const contraction of TREEBANK_CONTRACTIONS) {\n // Break uncommon contractions with a space and wrap-in spaces\n parse = parse.replace(contraction, ' $1 $2 ');\n }\n\n // Concatenate double spaces and remove start/end spaces\n parse = parse.replace(/ {2,}/g, ' ').replace(/^ | $/g, '');\n\n // Split on spaces (original and inserted) to return the tokenized result\n return parse.split(' ');\n}\n\n/**\n * Splits a body of text into an array of sentences\n * using a rule-based segmentation approach.\n *\n * Adapted from Spencer Mountain's nlp_compromise library\n * found at https://github.com/spencermountain/nlp_compromise/\n *\n * @method sentenceSegment\n * @param {string} input The document to be segmented\n * @return {Array<string>} An array of sentences\n */\n// biome-ignore lint/complexity/noExcessiveCognitiveComplexity: Sentence segmentation requires complex NLP logic\nexport function sentenceSegment(input: string): string[] {\n if (input.length === 0) {\n return [];\n }\n\n const abbrvReg = new RegExp(`\\\\b(${GATE_SUBSTITUTIONS.join('|')})[.!?] ?$`, 'i');\n const acronymReg = new RegExp(/[ |.][A-Z].?$/, 'i');\n const breakReg = new RegExp(/[\\r\\n]+/, 'g');\n // Match 2-10 dots at end of string (ellipsis pattern)\n // Using bounded {2,10} quantifier to avoid ReDoS with extremely long dot sequences\n const ellipseReg = /\\.{2,10}$/;\n const excepReg = new RegExp(`\\\\b(${GATE_EXCEPTIONS.join('|')})[.!?] ?$`, 'i');\n\n // Split sentences naively based on common terminals (.?!\")\n // Pattern uses a \"tempered greedy token\" to avoid ReDoS:\n // - (?:[^.?!\\r\\n]|[.?!](?!\\s|$|\"))* matches any char except newlines, OR a terminator NOT at a boundary\n // - [^.?!\\r\\n] excludes newlines to match original behavior (JS regex . doesn't match newlines)\n // - This allows matching through \"U.S.A.\" and \"$100.00\" without polynomial backtracking\n // - [.?!]{1,3} limits consecutive terminators (e.g., \"!!\", \"?!\", \"...\") to prevent ReDoS\n const chunks = input.split(/(\\S(?:[^.?!\\r\\n]|[.?!](?!\\s|$|\"))*[.?!]{1,3})(?=\\s|$|\")/g);\n\n const acc: string[] = [];\n for (let idx = 0; idx < chunks.length; idx++) {\n if (chunks[idx]) {\n // Trim only spaces (i.e. preserve line breaks/carriage feeds)\n // Note: Using separate replacements instead of alternation to avoid ReDoS\n chunks[idx] = chunks[idx].replace(/^ +/, '').replace(/ +$/, '');\n\n if (breakReg.test(chunks[idx])) {\n if (chunks[idx + 1] && strIsTitleCase(chunks[idx])) {\n // Catch line breaks embedded within valid sentences\n // i.e. sentences that start with a capital letter\n // and merge them with a delimiting space\n chunks[idx + 1] = `${chunks[idx].trim()} ${chunks[idx + 1].replace(/ +/g, ' ')}`;\n } else {\n // Assume that all other embedded line breaks are\n // valid sentence breakpoints\n acc.push(...chunks[idx].trim().split('\\n'));\n }\n } else if (chunks[idx + 1] && abbrvReg.test(chunks[idx])) {\n const nextChunk = chunks[idx + 1];\n if (nextChunk.trim() && strIsTitleCase(nextChunk) && !excepReg.test(chunks[idx])) {\n // Catch abbreviations followed by a capital letter and treat as a boundary.\n // FIXME: This causes named entities like `Mt. Fuji` or `U.S. Government` to fail.\n acc.push(chunks[idx]);\n chunks[idx] = '';\n } else {\n // Catch common abbreviations and merge them with a delimiting space\n chunks[idx + 1] = `${chunks[idx]} ${nextChunk.replace(/ +/g, ' ')}`;\n }\n } else if (chunks[idx].length > 1 && chunks[idx + 1] && acronymReg.test(chunks[idx])) {\n const words = chunks[idx].split(' ');\n const lastWord = words.at(-1)!;\n\n if (lastWord === lastWord.toLowerCase()) {\n // Catch small-letter abbreviations and merge them.\n chunks[idx + 1] = `${chunks[idx]} ${chunks[idx + 1].replace(/ +/g, ' ')}`;\n } else if (chunks[idx + 2]) {\n if (strIsTitleCase(words.at(-2)!) && strIsTitleCase(chunks[idx + 2])) {\n // Catch name abbreviations (e.g. Albert I. Jones) by checking if\n // the previous and next words are all capitalized.\n chunks[idx + 2] = chunks[idx] + chunks[idx + 1].replace(/ +/g, ' ') + chunks[idx + 2];\n } else {\n // Assume that remaining entities are indeed end-of-sentence markers.\n acc.push(chunks[idx]);\n chunks[idx] = '';\n }\n }\n } else if (chunks[idx + 1] && ellipseReg.test(chunks[idx])) {\n // Catch mid-sentence ellipses (and their derivatives) and merge them\n chunks[idx + 1] = chunks[idx] + chunks[idx + 1].replace(/ +/g, ' ');\n } else if (chunks[idx] && chunks[idx].length > 0) {\n acc.push(chunks[idx]);\n chunks[idx] = '';\n }\n }\n }\n\n // If no matches were found, return the input treated as a single sentence\n return acc.length === 0 ? [input] : acc;\n}\n\n/**\n * Checks if a string is titlecase\n * @method strIsTitleCase\n * @param {string} input The string to be checked\n * @return {boolean} True if the string is titlecase and false otherwise\n */\nexport function strIsTitleCase(input: string): boolean {\n const firstChar = input.trim().slice(0, 1);\n return charIsUpperCase(firstChar);\n}\n\n/**\n * Checks if a character is uppercase (i18n-compatible)\n * @method charIsUpperCase\n * @param {string} input The character to be tested\n * @return {boolean} True if the character is uppercase and false otherwise.\n */\nexport function charIsUpperCase(input: string): boolean {\n if (input.length !== 1) {\n throw new RangeError('Input should be a single character');\n }\n\n // Use locale-aware comparison to support international characters\n return input.toUpperCase() === input && input.toLowerCase() !== input;\n}\n\n/**\n * Memoizes a function using a Map\n *\n * **Memory Warning**: The cache is unbounded and will grow indefinitely for unique inputs.\n * In long-running processes with many unique inputs, consider using a bounded cache\n * implementation (e.g., LRU cache) or periodically clearing the memoized function.\n *\n * @method memoize\n * @param {Function} func The function to be memoized\n * @param {Function} Store The data store constructor. Defaults to the ES6-inbuilt Map function.\n * A store should implement `has`, `get`, and `set` methods.\n * @return {Function} A closure of the memoization cache and the original function\n */\nfunction memoize<T, R>(func: (arg: T) => R, Store: new () => Map<T, R> = Map): (arg: T) => R {\n return (() => {\n const cache = new Store();\n\n return (n: T) => {\n if (cache.has(n)) {\n const cachedResult = cache.get(n);\n if (cachedResult !== undefined) {\n return cachedResult;\n }\n }\n const result = func(n);\n cache.set(n, result);\n return result;\n };\n })();\n}\n\n/**\n * Computes the factorial of a number.\n *\n * This function uses a tail-recursive call to avoid\n * blowing the stack when computing inputs with a large\n * recursion depth.\n *\n * @method factRec\n * @param {number} x The number for which the factorial is to be computed\n * @param {number} acc The starting value for the computation. Defaults to 1.\n * @return {number} The factorial result\n */\nfunction factRec(x: number, acc = 1): number {\n if (x < 0) {\n throw new RangeError('Input must be a positive number');\n }\n return x < 2 ? acc : factRec(x - 1, x * acc);\n}\n\n/**\n * Memoized factorial function.\n *\n * **Memory Note**: Results are cached indefinitely. In typical ROUGE usage,\n * factorial is called with small values (\u226420) so memory impact is negligible.\n * The cache size is bounded by the range of valid factorial inputs that don't\n * overflow JavaScript's number type (approximately n \u2264 170).\n */\nexport const fact = memoize(factRec);\n\n/**\n * Returns the skip bigrams for an array of word tokens.\n *\n * @method skipBigram\n * @param {Array<string>} tokens An array of word tokens\n * @param {number} maxSkip Maximum skip distance between words. Defaults to Infinity (all pairs).\n * @return {Array<string>} An array of skip bigram strings\n */\nexport function skipBigram(tokens: string[], maxSkip: number = Number.POSITIVE_INFINITY): string[] {\n if (tokens.length < 2) {\n throw new RangeError('Input must have at least two words');\n }\n\n const acc: string[] = [];\n for (let baseIdx = 0; baseIdx < tokens.length - 1; baseIdx++) {\n const maxIdx = Math.min(baseIdx + 1 + maxSkip, tokens.length);\n for (let sweepIdx = baseIdx + 1; sweepIdx < maxIdx; sweepIdx++) {\n acc.push(`${tokens[baseIdx]} ${tokens[sweepIdx]}`);\n }\n }\n\n return acc;\n}\n\ninterface NGramOptions {\n start: boolean;\n end: boolean;\n val: string;\n}\n\nexport const NGRAM_DEFAULT_OPTS: NGramOptions = {\n start: false,\n end: false,\n val: '<S>',\n};\n\n/**\n * Returns n-grams for an array of word tokens.\n *\n * @method nGram\n * @param {Array<string>} tokens An array of word tokens\n * @param {number} n The size of the n-gram. Defaults to 2.\n * @param {Object} pad String padding options. See example.\n * @return {Array<string>} An array of n-gram strings\n */\nexport function nGram(tokens: string[], n = 2, pad: Partial<NGramOptions> = {}): string[] {\n if (n < 1) {\n throw new RangeError('ngram size cannot be smaller than 1');\n }\n\n if (tokens.length < n) {\n throw new RangeError('ngram size cannot be larger than the number of tokens available');\n }\n\n let workingTokens = tokens;\n\n if (Object.keys(pad).length > 0) {\n const config = { ...NGRAM_DEFAULT_OPTS, ...pad };\n\n // Clone the input token array to avoid mutating the source data\n const tempTokens = tokens.slice(0);\n\n if (config.start) {\n for (let i = 0; i < n - 1; i++) {\n tempTokens.unshift(config.val);\n }\n }\n if (config.end) {\n for (let i = 0; i < n - 1; i++) {\n tempTokens.push(config.val);\n }\n }\n\n workingTokens = tempTokens;\n }\n\n const acc: string[] = [];\n for (let idx = 0; idx < workingTokens.length - n + 1; idx++) {\n acc.push(workingTokens.slice(idx, idx + n).join(' '));\n }\n\n return acc;\n}\n\n/**\n * Calculates C(val, 2), i.e. the number of ways 2\n * items can be chosen from `val` items.\n *\n * @method comb2\n * @param {number} val The total number of items to choose from\n * @return {number} The number of ways in which 2 items can be chosen from `val`\n */\nexport function comb2(val: number): number {\n if (val < 2) {\n throw new RangeError('Input must be greater than 2');\n }\n return 0.5 * val * (val - 1);\n}\n\n/**\n * Computes the arithmetic mean of an array\n * @method arithmeticMean\n * @param {Array<number>} input Data distribution\n * @return {number} The mean of the distribution\n */\nexport function arithmeticMean(input: number[]): number {\n if (input.length === 0) {\n throw new RangeError('Input array must have at least 1 element');\n }\n return input.reduce((x, y) => x + y) / input.length;\n}\n\n/**\n * Evaluates the jackknife resampling result for a set of\n * candidate summaries vs. a reference summary.\n *\n * @method jackKnife\n * @param {Array<string>} cands An array of candidate summaries to be evaluated\n * @param {string} ref The reference summary to be evaluated against\n * @param {Function} func The function used to evaluate a candidate against a reference.\n * Should be of the type signature (string, string) => number\n * @param {Function} test The function used to compute the test statistic.\n * Defaults to the arithmetic mean.\n * Should be of the type signature (Array<number>) => number\n * @return {number} The result computed by applying `test` to the resampled data\n */\nexport function jackKnife(\n cands: string[],\n ref: string,\n func: (x: string, y: string) => number,\n test: (x: number[]) => number = arithmeticMean,\n): number {\n if (cands.length < 2) {\n throw new RangeError('Candidate array must contain more than one element');\n }\n\n const pairs: number[] = cands.map((c) => func(c, ref));\n\n const acc: number[] = [];\n for (let idx = 0; idx < pairs.length; idx++) {\n // Clone the array and remove one element\n const leaveOneOut = pairs.slice(0);\n leaveOneOut.splice(idx, 1);\n\n acc.push(Math.max(...leaveOneOut));\n }\n\n return test(acc);\n}\n\n/**\n * Calculates the ROUGE f-measure for a given precision\n * and recall score.\n *\n * Uses the standard F-beta formula:\n * F_\u03B2 = ((1 + \u03B2\u00B2) \u00D7 P \u00D7 R) / (\u03B2\u00B2 \u00D7 P + R)\n *\n * Beta controls the tradeoff between precision and recall:\n * - beta = 0: Pure precision (F\u2080 = P)\n * - beta = 1: F1 score (harmonic mean, equal weight)\n * - beta = 2: F2 score (weighs recall twice as much as precision)\n * - beta = Infinity: Pure recall\n *\n * @method fMeasure\n * @param {number} p Precision score (0 to 1)\n * @param {number} r Recall score (0 to 1)\n * @param {number} beta Weighing value (precision vs. recall). Defaults to 1.0 (F1).\n * @return {number} Computed f-score\n */\nexport function fMeasure(p: number, r: number, beta = 1.0): number {\n if (p < 0 || p > 1) {\n throw new RangeError('Precision value p must have bounds 0 \u2264 p \u2264 1');\n }\n if (r < 0 || r > 1) {\n throw new RangeError('Recall value r must have bounds 0 \u2264 r \u2264 1');\n }\n if (beta < 0) {\n throw new RangeError('beta value must be >= 0');\n }\n\n // Handle special cases\n if (p === 0 && r === 0) {\n return 0;\n }\n if (!Number.isFinite(beta)) {\n return r; // \u03B2 \u2192 \u221E means pure recall\n }\n\n const betaSq = beta * beta;\n const denominator = betaSq * p + r;\n if (denominator === 0) {\n return 0;\n }\n\n return ((1 + betaSq) * p * r) / denominator;\n}\n\n/**\n * Computes the set intersection of two arrays\n *\n * @method intersection\n * @template T\n * @param {Array<T>} a The first array\n * @param {Array<T>} b The second array\n * @return {Array<T>} Elements common to both the first and second array\n */\nexport function intersection<T>(a: T[], b: T[]): T[] {\n const test = new Set(a);\n const ref = new Set(b);\n\n return Array.from(test).filter((elem): elem is T => ref.has(elem));\n}\n\n/**\n * Computes the longest common subsequence for two arrays.\n * This function returns the elements from the two arrays\n * that form the LCS, in order of their appearance.\n *\n * For speed, the search-space is pruned by eliminating\n * common entities at the start and end of both input arrays.\n *\n * @method lcs\n * @param {Array<string>} a The first array\n * @param {Array<string>} b The second array\n * @return {Array<string>} The longest common subsequence between the first and second array\n */\nexport function lcs(a: string[], b: string[]): string[] {\n if (a.length === 0 || b.length === 0) {\n return [];\n }\n\n const dp: number[][] = new Array(a.length + 1).fill(0).map(() => new Array(b.length + 1).fill(0));\n\n for (let i = 1; i <= a.length; i++) {\n for (let j = 1; j <= b.length; j++) {\n if (a[i - 1] === b[j - 1]) {\n dp[i][j] = dp[i - 1][j - 1] + 1;\n } else {\n dp[i][j] = Math.max(dp[i - 1][j], dp[i][j - 1]);\n }\n }\n }\n\n const lcsResult: string[] = [];\n let i = a.length;\n let j = b.length;\n\n while (i > 0 && j > 0) {\n if (a[i - 1] === b[j - 1]) {\n lcsResult.unshift(a[i - 1]);\n i--;\n j--;\n } else if (dp[i - 1][j] > dp[i][j - 1]) {\n i--;\n } else {\n j--;\n }\n }\n\n return lcsResult;\n}\n", "import * as utils from './utils';\n\nexport * from './utils';\n\n/** Options for ROUGE-N evaluation */\nexport interface RougeNOptions {\n /** The size of the ngram used (default: 1) */\n n?: number;\n /** The beta value used for the f-measure (default: 1.0) */\n beta?: number;\n /** Whether comparison is case-sensitive (default: true) */\n caseSensitive?: boolean;\n /** Custom ngram generator function */\n nGram?: (tokens: string[], n: number) => string[];\n /** Custom string tokenizer */\n tokenizer?: (input: string) => string[];\n}\n\n/** Options for ROUGE-S (skip-bigram) evaluation */\nexport interface RougeSOptions {\n /** The beta value used for the f-measure (default: 1.0) */\n beta?: number;\n /** Whether comparison is case-sensitive (default: true) */\n caseSensitive?: boolean;\n /** Maximum skip distance between words (default: Infinity) */\n maxSkip?: number;\n /** Custom skip-bigram generator function */\n skipBigram?: (tokens: string[], maxSkip?: number) => string[];\n /** Custom string tokenizer */\n tokenizer?: (input: string) => string[];\n}\n\n/** Options for ROUGE-L (LCS) evaluation */\nexport interface RougeLOptions {\n /** The beta value used for the f-measure (default: 1.0) */\n beta?: number;\n /** Whether comparison is case-sensitive (default: true) */\n caseSensitive?: boolean;\n /** Custom LCS function */\n lcs?: (a: string[], b: string[]) => string[];\n /** Custom sentence segmenter */\n segmenter?: (input: string) => string[];\n /** Custom string tokenizer */\n tokenizer?: (input: string) => string[];\n}\n\n/**\n * Computes the ROUGE-N score for a candidate summary.\n *\n * Configuration object schema and defaults:\n * ```\n * {\n * \tn: 1 // The size of the ngram used\n * \tbeta: 1.0 // The beta value used for the f-measure\n * \tcaseSensitive: true // Whether comparison is case-sensitive\n * \tnGram: <inbuilt function>, // The ngram generator function\n * \ttokenizer: <inbuilt function> // The string tokenizer\n * }\n * ```\n *\n * `nGram` has a type signature of ((Array<string>, number) => Array<string>)\n * `tokenizer` has a type signature of ((string) => Array<string)\n *\n * @method n\n * @param {string} cand The candidate summary to be evaluated\n * @param {string} ref The reference summary to be evaluated against\n * @param {Object} opts Configuration options (see example)\n * @return {number} The ROUGE-N F-score\n */\nexport function n(cand: string, ref: string, opts?: RougeNOptions): number {\n if (cand.length === 0) {\n throw new RangeError('Candidate cannot be an empty string');\n }\n if (ref.length === 0) {\n throw new RangeError('Reference cannot be an empty string');\n }\n\n // Merge user-provided configuration with defaults\n const options = {\n n: 1,\n beta: 1.0,\n caseSensitive: true,\n nGram: utils.nGram,\n tokenizer: utils.treeBankTokenize,\n ...opts,\n };\n\n const candText = options.caseSensitive ? cand : cand.toLowerCase();\n const refText = options.caseSensitive ? ref : ref.toLowerCase();\n\n const candGrams = options.nGram(options.tokenizer(candText), options.n);\n const refGrams = options.nGram(options.tokenizer(refText), options.n);\n\n const match = utils.intersection(candGrams, refGrams);\n\n if (match.length === 0) {\n return 0;\n }\n\n const precision = match.length / candGrams.length;\n const recall = match.length / refGrams.length;\n\n return utils.fMeasure(precision, recall, options.beta);\n}\n\n/**\n * Computes the ROUGE-S score for a candidate summary.\n *\n * Configuration object schema and defaults:\n * ```\n * {\n * \tbeta: 1.0 // The beta value used for the f-measure\n * \tcaseSensitive: true // Whether comparison is case-sensitive\n * \tmaxSkip: Infinity // Maximum skip distance between words\n * \tskipBigram: <inbuilt function>, // The skip-bigram generator function\n * \ttokenizer: <inbuilt function> // The string tokenizer\n * }\n * ```\n *\n * `skipBigram` has a type signature of ((Array<string>, number) => Array<string>)\n * `tokenizer` has a type signature of ((string) => Array<string)\n *\n * @method s\n * @param {string} cand The candidate summary to be evaluated\n * @param {string} ref The reference summary to be evaluated against\n * @param {Object} opts Configuration options (see example)\n * @return {number} The ROUGE-S score\n */\nexport function s(cand: string, ref: string, opts?: RougeSOptions): number {\n if (cand.length === 0) {\n throw new RangeError('Candidate cannot be an empty string');\n }\n if (ref.length === 0) {\n throw new RangeError('Reference cannot be an empty string');\n }\n\n // Merge user-provided configuration with defaults\n const options = {\n beta: 1.0,\n caseSensitive: true,\n maxSkip: Number.POSITIVE_INFINITY,\n skipBigram: utils.skipBigram,\n tokenizer: utils.treeBankTokenize,\n ...opts,\n };\n\n const candText = options.caseSensitive ? cand : cand.toLowerCase();\n const refText = options.caseSensitive ? ref : ref.toLowerCase();\n\n const candGrams = options.skipBigram(options.tokenizer(candText), options.maxSkip);\n const refGrams = options.skipBigram(options.tokenizer(refText), options.maxSkip);\n\n const skip2 = utils.intersection(candGrams, refGrams).length;\n\n if (skip2 === 0) {\n return 0;\n }\n const skip2Recall = skip2 / refGrams.length;\n const skip2Prec = skip2 / candGrams.length;\n\n return utils.fMeasure(skip2Prec, skip2Recall, options.beta);\n}\n\n/**\n * Computes the ROUGE-L score for a candidate summary\n *\n * Configuration object schema and defaults:\n * ```\n * {\n * \tbeta: 1.0 // The beta value used for the f-measure\n * \tcaseSensitive: true // Whether comparison is case-sensitive\n * \tlcs: <inbuilt function> // The least common subsequence function\n * \tsegmenter: <inbuilt function>, // The sentence segmenter\n * \ttokenizer: <inbuilt function> // The string tokenizer\n * }\n * ```\n *\n * `lcs` has a type signature of ((Array<string>, Array<string>) => Array<string>)\n * `segmenter` has a type signature of ((string) => Array<string)\n * `tokenizer` has a type signature of ((string) => Array<string)\n *\n * @method l\n * @param {string} cand The candidate summary to be evaluated\n * @param {string} ref The reference summary to be evaluated against\n * @param {Object} opts Configuration options (see example)\n * @return {number} The ROUGE-L score\n */\nexport function l(cand: string, ref: string, opts?: RougeLOptions): number {\n if (cand.length === 0) {\n throw new RangeError('Candidate cannot be an empty string');\n }\n if (ref.length === 0) {\n throw new RangeError('Reference cannot be an empty string');\n }\n\n // Merge user-provided configuration with defaults\n const options = {\n beta: 1.0,\n caseSensitive: true,\n lcs: utils.lcs,\n segmenter: utils.sentenceSegment,\n tokenizer: utils.treeBankTokenize,\n ...opts,\n };\n\n const candText = options.caseSensitive ? cand : cand.toLowerCase();\n const refText = options.caseSensitive ? ref : ref.toLowerCase();\n\n const candSents = options.segmenter(candText);\n const refSents = options.segmenter(refText);\n\n const candWords = options.tokenizer(candText);\n const refWords = options.tokenizer(refText);\n\n const lcsAcc = refSents.map((r) => {\n const rTokens = options.tokenizer(r);\n const lcsUnion = new Set(candSents.flatMap((c) => options.lcs(options.tokenizer(c), rTokens)));\n\n return lcsUnion.size;\n });\n\n // Sum the array as quickly as we can\n let lcsSum = 0;\n while (lcsAcc.length > 0) {\n lcsSum += lcsAcc.pop() || 0;\n }\n\n if (lcsSum === 0) {\n return 0;\n }\n\n // Recall = LCS / |reference| (how much of reference is captured)\n // Precision = LCS / |candidate| (how precise is the candidate)\n const lcsRecall = lcsSum / refWords.length;\n const lcsPrec = lcsSum / candWords.length;\n\n return utils.fMeasure(lcsPrec, lcsRecall, options.beta);\n}\n"], |
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