# Text Summarizer API - Extractive TextRank, No LLM (`eliai/text-summarizer`) Actor Text summarization API. Input: long text plus an optional sentence-count/length control. Output: JSON with the extracted top sentences (TextRank) as a summary. Deterministic, extractive, no LLM, no API key. Pay per result: $0.03 per text summarized. - **URL**: https://apify.com/eliai/text-summarizer.md - **Developed by:** [Anthony Snider](https://apify.com/eliai) (community) - **Categories:** Developer tools, AI, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing $30.00 / 1,000 text summarizeds This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Extractive Text Summarizer Turn long text into its most important sentences — instantly, deterministically, with **no LLM and no API key**. **Live on the Apify Store — run it instantly or call it as an agent tool via Apify MCP.** ### What you get - **TextRank summarization** — a graph-based (PageRank-style) ranking over a sentence-similarity graph picks the sentences that best represent the whole text. - **No LLM, no API key, no rate limits** — pure algorithm, fully **deterministic**: the same input always yields the same summary. - **Length control** — set an exact sentence count or a ratio of the original. - **Single or bulk** — summarize one text or up to 50 in a single run. - **Original order preserved** — selected sentences are returned in the order they appeared, so the summary reads naturally. ### Input ```json { "text": "TextRank is an unsupervised, graph-based ranking algorithm for text. It builds a graph where each sentence is a node, and edges are weighted by sentence similarity. The algorithm runs a PageRank-style power iteration over this graph. Sentences similar to many other important sentences receive higher scores. It needs no training data, no API key, and no language model.", "sentences": 2 } ```` - `text` (string) — the text to summarize. *(or)* - `texts` (array of strings) — bulk mode, up to 50 texts. - `sentences` (integer, default `3`) — how many sentences to keep. - `ratio` (number 0–1, optional) — keep this fraction of the original instead of a fixed count (overrides `sentences`). ### Output One dataset item per input text: ```json { "summary": "TextRank is an unsupervised, graph-based ranking algorithm for text. It needs no training data, no API key, and no language model.", "sentenceCount": 2, "selectedIndices": [0, 4], "originalSentenceCount": 5 } ``` - `summary` — the extracted sentences, joined in original order. - `sentenceCount` — number of sentences in the summary. - `selectedIndices` — indices (0-based) of the chosen sentences in the original text. - `originalSentenceCount` — total sentences detected in the input. # Actor input Schema ## `text` (type: `string`): The text to summarize. Paste an article, document, or transcript. ## `texts` (type: `array`): Optional. An array of texts to summarize in one run (up to 50). Each produces its own summary. ## `sentences` (type: `integer`): How many sentences the summary should contain. Default 3. Ignored if 'ratio' is set. ## `ratio` (type: `number`): Optional. Keep this fraction (0-1) of the original sentences instead of a fixed count. Overrides 'sentences'. ## Actor input object example ```json { "text": "TextRank is an unsupervised, graph-based ranking algorithm for text. It builds a graph where each sentence is a node, and edges are weighted by how similar two sentences are. The algorithm then runs a PageRank-style power iteration over this graph. Sentences that are similar to many other important sentences receive higher scores. Because it relies only on the structure of the text itself, TextRank needs no training data, no API key, and no language model. The result is fully deterministic: the same input always produces the same summary. This makes it a fast, free, and reproducible way to extract the most representative sentences from long documents.", "sentences": 3 } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "text": "TextRank is an unsupervised, graph-based ranking algorithm for text. It builds a graph where each sentence is a node, and edges are weighted by how similar two sentences are. The algorithm then runs a PageRank-style power iteration over this graph. Sentences that are similar to many other important sentences receive higher scores. Because it relies only on the structure of the text itself, TextRank needs no training data, no API key, and no language model. The result is fully deterministic: the same input always produces the same summary. This makes it a fast, free, and reproducible way to extract the most representative sentences from long documents." }; // Run the Actor and wait for it to finish const run = await client.actor("eliai/text-summarizer").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "text": "TextRank is an unsupervised, graph-based ranking algorithm for text. It builds a graph where each sentence is a node, and edges are weighted by how similar two sentences are. The algorithm then runs a PageRank-style power iteration over this graph. Sentences that are similar to many other important sentences receive higher scores. Because it relies only on the structure of the text itself, TextRank needs no training data, no API key, and no language model. The result is fully deterministic: the same input always produces the same summary. This makes it a fast, free, and reproducible way to extract the most representative sentences from long documents." } # Run the Actor and wait for it to finish run = client.actor("eliai/text-summarizer").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "text": "TextRank is an unsupervised, graph-based ranking algorithm for text. It builds a graph where each sentence is a node, and edges are weighted by how similar two sentences are. The algorithm then runs a PageRank-style power iteration over this graph. Sentences that are similar to many other important sentences receive higher scores. Because it relies only on the structure of the text itself, TextRank needs no training data, no API key, and no language model. The result is fully deterministic: the same input always produces the same summary. This makes it a fast, free, and reproducible way to extract the most representative sentences from long documents." }' | apify call eliai/text-summarizer --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=eliai/text-summarizer", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Text Summarizer API - Extractive TextRank, No LLM", "description": "Text summarization API. Input: long text plus an optional sentence-count/length control. Output: JSON with the extracted top sentences (TextRank) as a summary. Deterministic, extractive, no LLM, no API key. Pay per result: $0.03 per text summarized.", "version": "0.1", "x-build-id": "M0GJCeD16SH2RS49a" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/eliai~text-summarizer/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-eliai-text-summarizer", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/eliai~text-summarizer/runs": { "post": { "operationId": "runs-sync-eliai-text-summarizer", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/eliai~text-summarizer/run-sync": { "post": { "operationId": "run-sync-eliai-text-summarizer", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "text": { "title": "Text", "type": "string", "description": "The text to summarize. Paste an article, document, or transcript." }, "texts": { "title": "Texts (bulk)", "type": "array", "description": "Optional. An array of texts to summarize in one run (up to 50). Each produces its own summary.", "items": { "type": "string" } }, "sentences": { "title": "Sentences", "minimum": 1, "type": "integer", "description": "How many sentences the summary should contain. Default 3. Ignored if 'ratio' is set.", "default": 3 }, "ratio": { "title": "Ratio", "minimum": 0.01, "maximum": 1, "type": "number", "description": "Optional. Keep this fraction (0-1) of the original sentences instead of a fixed count. Overrides 'sentences'." } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```